Statistics and Probability Catalogue 2015

Statistics and Probability 2015

cambridge.org/statistics2015

Welcome to the Statistics and Probability books catalogue 2015.

Here you will find new and forthcoming titles, representing the highest level of academic research from renowned authors. Our highlights this year include exciting new works such as Causal Inference in Statistics, Social, and Biomedical Sciences from Guido W. Imbens and Donald B. Rubin and Privacy, Big Data, and the Public Good by Julia Lane et al.

Our publications are available in a variety of formats, including ebooks and print, as well as online collections for institutional purchase via our publishing service University Publishing Online, which incorporates the Cambridge Books Online platform.

We also publish a range of leading Statistics and Probability journals, including The British Actuarial Journal (see back inside page for more information). You can recommend our books, online collections and journals to your librarian by filling out the form at the back of this catalogue.

To see more book listings, product information, preview extracts and reviews, and to find out which conferences we are attending, you can find us online at www.cambridge.org/statistics2015.

You can also keep up to date with the latest news and author views from our academic blog at http://www.cambridgeblog.org/.

We hope that you enjoy reading about our latest publications. For queries, suggestions or proposals, you can find a list of useful contacts at the back of this catalogue.

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Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions.

GUIDO W. IMBENS is Professor of Economics at the Graduate School of Business,

Stanford University. He has held tenured faculty positions at Harvard University, UCLA, UC Berkeley, and Stanford University. He is a fellow of the Econometric Society and the American Academy of Arts and Sciences. Imbens has published widely in economics and statistics journals, including Econometrica, The American Economic Review, the Annals of Statistics, the Journal of the American Statistical Association, Biometrika, and the Journal of the Royal Statistical Society.

Statistical theory and methods 1 Statistics for environmental sciences 5 Statistics for life sciences, medicine and health 5 Statistics for physical sciences and engineering 6 Knowledge management, databases, datamining 7 Computational statistics, machine learning and information science 8 Statistics for econometrics, finance and insurance 9 Econometrics, statistics and mathematical economics 10 Mathematical modelling and methods 15 Probability theory and stochastic processes 15 Applied probability and stochastic networks 19 Optimization, OR and risk analysis 20 Also of interest 21 Information on related journals Inside back cover

DONALD B. RUBIN is John L. Loeb Professor of Statistics at Harvard University,

where he has been professor since 1983 and department chair for thirteen of those years. He has authored or coauthored nearly four hundred publications (including ten books), has four joint patents, and has made important contributions to statistical theory and methodology, particularly in causal inference, design and analysis of experiments and sample surveys, treatment of missing data, and Bayesian data analysis. Rubin has received the Samuel S. Wilks Medal from the American Statistical Association, the Parzen Prize for Statistical Innovation, the Fisher Lectureship, and the George W. Snedecor Award from the Committee of Presidents of Statistical Societies. He was named Statistician of the Year by the American Statistical Association, Boston and Chicago chapters. He is one of the most highly cited authors in mathematics and economics with nearly 150,000 citations to date.

CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES

This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes.

The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies.

They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

IMBENS • RUBIN

Contents

CAUSAL INFERENCE

see page 1

FOR

STATISTICS, SOCIAL, AND

BIOMEDICAL SCIENCES AN INTRODUCTION

GUIDO W. IMBENS DONALD B. RUBIN

Cover design by Alice Soloway

• Provides a solid foundation in modern risk management techniques.

• Assumes only basic calculus and linear algebra as pre-requisites.

• Exercises range from simple verification to more challenging problems. maciej j. capinski is an associate professor in the Faculty of Applied Mathematics at AGH University of Science and Technology in Kraków, Poland. He has authored 10 research publications, one book, and supervised over 30 MSc dissertations, mostly in mathematical finance. ekkehard kopp is Emeritus Professor of Mathematics at the University of Hull. He has published over 50 research papers and five books, on measure and probability, stochastic analysis and mathematical finance. He has taught in the UK, Canada and South Africa, and he serves on the editorial board of the AIMS Library Series.

Predictive Modeling Applications in Actuarial Science

see page 9

Volume I: Predictive Modeling Techniques Edited by

Edward W. Frees, Richard A. Derrig, Glenn Meyers

Mastering Mathematical Finance is a series of short books that cover all core topics and the most common electives offered in Master’s programmes in mathematical or quantitative finance. The books are closely coordinated and largely selfcontained, and can be used efficiently in combination but also individually.

The MMF books start financially from scratch and mathematically assume only undergraduate calculus, linear algebra and elementary probability theory.

The necessary mathematics is developed rigorously, with emphasis on a natural development of mathematical ideas and financial intuition, and the readers quickly see real-life financial applications, both for motivation and as the ultimate end for the theory. All books are written for both teaching and self-study, with worked examples, exercises and solutions.

Portfolio Theory and Risk Management

A moderate pace, careful motivation and more than 70 exercises give students confidence in handling risk assessments in modern finance. Solutions and additional materials for instructors are available at www.cambridge. org/9781107003675.

International Series on Actuarial Science

capiński & kopp

9781107003675 Capinski & Kopp PPC C M Y K

With its emphasis on examples, exercises and calculations, this book suits advanced undergraduates as well as postgraduates and practitioners. It provides a clear treatment of the scope and limitations of mean-variance portfolio theory and introduces popular modern risk measures. Proofs are given in detail, assuming only modest mathematical background, but with attention to clarity and rigour. The discussion of VaR and its more robust generalisations, such as AVaR, brings recent developments in risk measures within range of some undergraduate courses and includes a novel discussion of reducing VaR and AVaR by means of hedging techniques.

see page 2

MASTERING MATHEMATICAL FINANCE

Portfolio Theory and Risk Management

see page 10

´ MACIEJ J. CAPINSKI EKKEHARD KOPP

Probability and Statistics

by Example

1 Basic Probability and Statistics second edition

Yuri Suhov and Mark kElbErt

see page 16

Mastering Mathematical Finance

MAST ERING

MATH EMAT ICAL

FINAN CE

Stochastic Interest Rates NER NEY DAR AGH MCI AWN IAK TOM ASZ ZAST

General Editors: Marek Capiński, University of Science and Technology, Krakow; Ekkehard Kopp, University of Hull; Tomasz Zastawniak, University of York

MASTERING MATHEMATICAL FINANCE

The Black–Scholes Model ´ MAREK CAPINSKI EKKEHARD KOPP

Results are proved carefully, and the key concepts are motivated by concrete examples drawn from financial market models. Students can test their understanding through the large number of exercises that are integral to the text.

• Key concepts of probability are developed directly in the context of financial models • Careful motivation and plentiful exercises help students gain confidence • Solutions to exercises are available online

Mastering Mathematical Finance (MMF) is a series of short books that cover all core topics and the most common electives offered in Master’s programmes in mathematical or quantitative finance. The books are closely coordinated and largely self-contained, and can be used efficiently in combination but also individually.

MASTERING MATHEMATIC AL

M Y K

The MMF books start financially from scratch and mathematically assume only undergraduate calculus, linear algebra and elementary probability theory.

The necessary mathematics is developed rigorously, with emphasis on a natural development of mathematical ideas and financial intuition, and the readers quickly see real-life financial applications, both for motivation and as the ultimate end for the theory. All books are written for both teaching and self-study, with worked examples, exercises and solutions.

ekkehard kopp is Emeritus Professor of Mathematics at the University of Hull. He has published over 50 research papers and five books, on measure and probability, stochastic analysis and mathematical finance. He has taught in the UK, Canada and South Africa, and he serves on the editorial board of the AIMS Library Series. jan malczak has published over 20 research papers and taught courses in analysis, differential equations, measure and probability, and the theory of stochastic differential processes. He is currently Professor of Mathematics at AGH University of Science and Technology in Kraków, Poland. tomasz zastawniak holds the Chair of Mathematical Finance at the University of York. He has authored about 50 research publications and four books. He has supervised four PhD dissertations and around 80 MSc dissertations in mathematical finance.

FINANCE

Probability for Finance

Probability for Finance

Kopp 9780521175579 Cover. C

A series of short books that cover all the core topics and the most common electives offered in master’s programs in mathematical/quantitative finance. The books are closely coordinated but each self-contained, so that they can be used efficiently in combination but also individually.

kopp, malczak & zastawniak

Students and instructors alike will benefit from this rigorous, unfussy text, which keeps a clear focus on the basic probabilistic concepts required for an understanding of financial market models, including independence and conditioning. Assuming only some calculus and linear algebra, the text applies key results of measure and integration to probability spaces and random variables, culminating in Central Limit Theory. Consequently, it provides essential pre-requisites to graduate-level study of modern finance and, more generally, to the study of stochastic processes.

EKKEHARD KOPP JAN MALCZAK TOMASZ ZASTAWNIAK

cMyk

PPc.

9781107044074.

& Waters –

Dickson, HarDy

SECON D EDITI

International Series on Actuarial Science

Solutions Manual for Actuarial Mathematics for Life Contingent Risks

SECOND EDITION

Actuarial Mathematics for t Life Contingen Risks

Solutions Manual for Actuarial Mathematics for Life Contingent Risks SECOND EDITION

David C. M. Dickson, Mary R. Hardy and Howard R. Waters

ON

on, Mary R. Hardy David C. M. Dicks Waters and Howard R.

and valuation.’

Journal Scandinavian Actuarial

Cover designed

by Hart McLeod

Ltd

PPC. C M Y BLK

SWEETING: FINANCIAL ENTERPRISE.

The tools of modelling and simulation are outlined in Part I with special emphasis on the Monte Carlo method and its use. Part II deals with general insurance and Part III with life insurance and financial risk. Algorithms that can be implemented on any programming platform are spread throughout, and a program library written in R is included.

Numerous figures and experiments with R code illustrate the text.

The author’s non-technical approach is ideal for graduate students, the only prerequisites being introductory courses in calculus and linear algebra, probability and statistics. The book will also be useful for actuaries and other analysts in the industry looking to update their skills.

Cover designed by Hart McLeod Ltd

Predictive Modeling Applications in Actuarial Science Volume I: Predictive Modeling Techniques Edited by

Edward W. Frees, Richard A. Derrig, Glenn Meyers

Computation and Modelling in Insurance and Finance

Scientific computing is as critical for the analysis of risk in insurance and finance as are mathematics and statistics, and it should be taught jointly with them.

This book offers such an integrated approach at an introductory level and provides readers with much of what is needed in practice, including how simulation programs are designed, used and reused (with modifications) as situations change. Complex problems with risk from many sources are discussed, as is the sensitivity of conclusions on assumptions and historical data.

International Series on Actuarial Science

Bølviken

The International Series on Actuarial Science, published by Cambridge University Press in conjunction with the Institute and Faculty of Actuaries, contains textbooks for students taking courses in or related to actuarial science, as well as more advanced works designed for continuing professional development or for describing and synthesizing research. The series is a vehicle for publishing books that reflect changes and developments in the curriculum, that encourage the introduction of courses on actuarial science in universities, and that show how actuarial science can be used in all areas where there is long-term financial risk.

www.cambridge.org/isas

Dickson, Hardy and Waters

It may be an and easy to read. written, well organized, programmes ‘The book is well ate and graduate for both undergradu information for excellent textbook source of useful It is also a rich managers in actuarial science. and financial risk the actuarial profession flow modelling practitioners of to liability cash and inspiring guide who seek a practical

SECOND EDITION

Editorial Board: Christopher Daykin, Independent Consultant and Actuary; Angus Macdonald, Heriot-Watt University

Interna

matics for Actuarial Mathe Risks Life Contingent

and risk structures for the products actuarial best equip themselves three leaders in How can actuaries aking textbook, this ground-bre es. of the future? In life contingenci perspective on emphasizing practical science give a modern models and theory, a with actuarial authors then develop The book begins techniques. The computational models, emerging applications using multiple state ry outlook, introducing contains more edition more contempora This expanded well as embedded options. preparation as cash flows and to help with exam chapters exercises designed new sections and brand examples and are on expertise. There joint lives and developing up-to-date on models involving Markov processes, insurance. on discrete time traditional g and participatin this g applications, Universal Life insurance and emphasizin with intuition, wishing Balancing rigour for qualified actuaries for university courses, preparing for the textbook is ideal for individuals their skills and or Institute to renew and update the Society of Actuaries examinations of syllabus and entire SOA MLC professional actuarial book covers the Actuaries. The new, long and Faculty of preparing for the valuable for students will be especially answer exam questions.

This must-have manual provides detailed solutions to all of the 200+ exercises in Dickson, Hardy and Waters’ Actuarial Mathematics for Life Contingent Risks, Second Edition. This ground-breaking text on the modern mathematics of life insurance is required reading for the Society of Actuaries’ Exam MLC and also provides a solid preparation for the life contingencies material of the UK actuarial profession’s Exam CT5.

Beyond the professional examinations, the textbook and solutions manual offer readers the opportunity to develop insight and understanding, and also offer practical advice for solving problems using straightforward, intuitive numerical methods.

Companion spreadsheets illustrating Actuarial Science tional Series onthese techniques are available for free download.

Dickson, Hardy and Waters

International Series on Actuarial Science

9781107620261 DICKSON – SOlutIONS MaNual fOr aCtuarIal MatheMatICS fOr lIfe CONtINgeNt rISKS C M Y K

www.cambridge.org/mmf

International Series on Actuarial Science

Computation and Modelling in Insurance and Finance Erik Bølviken

Statistical theory and methods

Statistical theory and methods Confidence, Likelihood and Probability Tore Schweder Universitetet i Oslo

and Nils Lid Hjort Universitetet i Oslo

This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. Fisher’s concept of fiducial probability plays an important role.

2015 253 x 177 mm 450pp 110 b/w illus. 100 exercises 978-0-521-86160-1 Hardback c. £50.00 / c. US$80.00 Publication September 2015 For all formats available, see

www.cambridge.org/9780521861601

1

Highlight

Causal Inference for Statistics, Social, and Biomedical Sciences An Introduction Guido W. Imbens Stanford University, California

and Donald B. Rubin Harvard University, Massachusetts

In this groundbreaking text, two worldrenowned experts present statistical methods for studying causal effects: how can we learn about the expected effect of an intervention or a change in environment? The authors discuss how we can assess such effects in simple randomized experiments, where the researcher controls the treatments, and in observational studies, where the subjects themselves may affect which treatment they receive.

Advance praise: ‘This book offers a definitive treatment of causality using the potential outcomes approach. Both theoreticians and applied researchers will find this an indispensable volume for guidance and reference.’

Hal Varian, Chief Economist, Google and Emeritus Professor, University of California, Berkeley 2015 253 x 177 mm 592pp 18 b/w illus. 97 tables 978-0-521-88588-1 Hardback £40.00 / US$60.00 Publication May 2015 For all formats available, see

www.cambridge.org/9780521885881

eBooks available at www.cambridge.org/ebookstore

2

Statistical theory and methods Modeling Count Data Joseph M. Hilbe Arizona State University

Written for researchers with little or no background in advanced statistics, this book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models. Stata, R, and SAS code enable readers in a variety of disciplines to adapt models for their own purposes.

‘This is a first-rate introductory book for modeling count data, a key challenge in applied statistics.

Hilbe’s experience and affability shine in the text. His careful emphasis on establishing the defensibility of models, for example, in the face of overdispersion, will greatly benefit the beginning statistician. His clear informal explanations of important and complicated statistical principles are invaluable.’

Andrew Robinson, University of Melbourne 2014 234 x 177 mm 297pp 10 b/w illus. 81 tables 978-1-107-02833-3 Hardback £60.00 / US$99.00 978-1-107-61125-2 Paperback £23.99 / US$37.99 For all formats available, see

www.cambridge.org/9781107028333

Privacy, Big Data, and the Public Good Frameworks for Engagement Edited by Julia Lane American Institutes for Research, Washington DC

Victoria Stodden Columbia University, New York

Stefan Bender Institute for Employment Research of the German Federal Employment Agency

and Helen Nissenbaum New York University

The book discusses access to big data for city, state, and federal government agencies and legal, social science, statistical, and technical communities interested in enabling research on big data. The authors’ goal is to move the conversation towards a vision of what frameworks should and could guide data access.

“Big data’ – the collection, aggregation or federation, and analysis of vast amounts of increasingly granular data – present[s] serious challenges not only to personal privacy but also to the tools we use to protect it. Privacy, Big Data, and the Public Good focuses valuable attention on two of these tools: notice and consent, and de-identification – the process of preventing a person’s identity from being linked to specific data. [It] presents a collection of essays from a variety of perspectives, in chapters by some of the heavy hitters in the privacy debate …’

Science 2014 228 x 152 mm 344pp 4 b/w illus.

978-1-107-06735-6 Hardback £55.00 / US$85.00 978-1-107-63768-9 Paperback £19.99 / US$29.99 For all formats available, see

www.cambridge.org/9781107067356

Statistical theory and methods Counterfactuals and Causal Inference Methods and Principles for Social Research Second edition Stephen L. Morgan The Johns Hopkins University

and Christopher Winship Harvard University, Massachusetts

Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.

‘The use of counterfactuals for causal inference has brought clarity to our reasoning about causality. And this second edition by Morgan and Winship will bring clarity to anyone trying to learn about the field. It is an excellent introduction to the topic, and a fine place to begin learning causal inference.’

Tyler J. VanderWeele, Harvard University, Massachusetts Analytical Methods for Social Research

2015 253 x 177 mm 524pp 64 b/w illus.

978-1-107-06507-9 Hardback £65.00 / US$99.00 978-1-107-69416-3 Paperback £24.99 / US$39.99 For all formats available, see

www.cambridge.org/9781107065079

3

Nonparametric Estimation under Shape Constraints Estimators, Algorithms and Asymptotics Piet Groeneboom Technische Universiteit Delft, The Netherlands

and Geurt Jongbloed Technische Universiteit Delft, The Netherlands

This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

‘Shape constraints arise naturally in many statistical applications and are becoming increasingly popular as a means of combining the best of the parametric and nonparametric worlds.

This book, written by two experts in the field, gives a detailed treatment of many of their attractive features.

I have no doubt it will be a valuable resource for researchers, students, and others interested in learning about this fascinating area.’

Richard Samworth, University of Cambridge Cambridge Series in Statistical and Probabilistic Mathematics, 38

2014 253 x 177 mm 428pp 90 b/w illus. 20 tables 190 exercises 978-0-521-86401-5 Hardback £55.00 / US$85.00 For all formats available, see

www.cambridge.org/9780521864015

For regular email alerts visit www.cambridge.org/alerts

4

Statistical theory and methods Large Sample Covariance Matrices and High-Dimensional Data Analysis

Case-Control Studies

Jianfeng Yao

This book covers the fundamentals of case-control studies including important recent developments, with a focus on statistical analysis.

The University of Hong Kong

Shurong Zheng Northeast Normal University, China

and Zhidong Bai Northeast Normal University, China

High-dimensional statistical methods are at the heart of the new era of big data analytics. This book, written by leading experts, is highly recommended for anyone who wants to make serious use of these modern statistical tools.

Advance praise: ‘This is the first book which treats systematic corrections to the classical multivariate statistical procedures so that the resultant procedures can be used for high-dimensional data.

The corrections have been done by employing asymptotic tools based on the theory of random matrices.’

Yasunori Fujikoshi, Hiroshima University Cambridge Series in Statistical and Probabilistic Mathematics, 39

2015 253 x 177 mm 286pp 80 b/w illus. 30 tables 978-1-107-06517-8 Hardback £52.00 / US$85.00 Publication April 2015 For all formats available, see

www.cambridge.org/9781107065178

Ruth H. Keogh London School of Hygiene and Tropical Medicine

and D. R. Cox University of Oxford

Institute of Mathematical Statistics Monographs, 4

2014 228 x 152 mm 293pp 30 b/w illus. 20 tables 978-1-107-01956-0 Hardback £50.00 / US$80.00 For all formats available, see

www.cambridge.org/9781107019560

Core Statistics Simon Wood University of Bath

Core Statistics is a compact starter course on the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. It delivers the theory and tools that a beginning graduate student, or any quantitative scientist, needs to make informed use of powerful statistical methods.

Institute of Mathematical Statistics Textbooks, 6

2015 228 x 152 mm 258pp 43 b/w illus. 2 tables 51 exercises 978-1-107-07105-6 Hardback £60.00 / US$99.00 978-1-107-41504-1 Paperback £24.99 / US$39.99 Publication April 2015 For all formats available, see

www.cambridge.org/9781107071056

Statistics for environmental sciences / Statistics for life sciences, medicine and health

Statistics for environmental sciences Measuring and Reasoning Numerical Inference in the Sciences Fred L. Bookstein University of Washington and Universität Wien, Austria

This exploration of empirical inference presents descriptions of the processes by which scientific measurements support explanations of our world.

2014 234 x 156 mm 559pp 180 b/w illus.

978-1-107-02415-1 Hardback £50.00 / US$75.00 For all formats available, see

www.cambridge.org/9781107024151

5

Statistics for life sciences, medicine and health Integrating Omics Data George C. Tseng University of Pittsburgh

Debashis Ghosh Pennsylvania State University

and Xianghong Jasmine Zhou University of Southern California

This book provides comprehensive coverage of information integration of omics, experimental data, and databases. It introduces state-of-the-art methods developed by leaders in the field to handle information integration problems of omics data. Popular technologies include microarray, nextgeneration sequencing, and proteomic experiments.

2015 228 x 152 mm 488pp 147 b/w illus. 23 colour illus. 31 tables 978-1-107-06911-4 Hardback £80.00 / US$125.00 Publication August 2015 For all formats available, see

www.cambridge.org/9781107069114

Visit our website at www.cambridge.org/academic

6

Statistics for life sciences, medicine and health / Statistics for physical sciences and engineering The Design and Statistical Analysis of Animal Experiments

includes a wealth of worked examples and end-of-chapter exercises.

GlaxoSmithKline

2015 247 x 174 mm 800pp 315 b/w illus. 19 tables 198 exercises 978-1-107-00339-2 Hardback £65.00 / US$99.00

and Robin A. Clark

Publication February 2015

Simon T. Bate Huntingdon Life Sciences

This book will provide scientists with a better understanding of statistics, improving their decision-making and reducing animal use.

2014 246 x 189 mm 324pp 227 b/w illus. 87 tables 978-1-107-03078-7 Hardback £70.00 / US$115.00

For all formats available, see

www.cambridge.org/9781107003392

Bayesian Probability Theory Applications in the Physical Sciences Wolfgang von der Linden

978-1-107-69094-3 Paperback £35.00 / US$55.00

Technische Universität Graz, Austria

For all formats available, see

Max-Planck-Institut für Plasmaphysik, Garching, Germany

www.cambridge.org/9781107030787

Volker Dose and Udo von Toussaint

Statistics for physical sciences and engineering Sampling Theory Beyond Bandlimited Systems Yonina C. Eldar Technion – Israel Institute of Technology, Haifa

Covering the fundamental mathematical underpinnings together with engineering principles and applications, this is a comprehensive guide to the theory and practice of sampling. Written from an engineering perspective, it focuses on uniform sampling in shift-invariant spaces and deterministic signals, and

Max-Planck-Institut für Plasmaphysik, Garching, Germany

Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective, this book is ideal for graduate students and researchers.

It presents the roots, applications and numerical implementation of probability theory, covers advanced topics and features real-world problems.

2014 247 x 174 mm 649pp 128 b/w illus.

978-1-107-03590-4 Hardback £60.00 / US$95.00 For all formats available, see

www.cambridge.org/9781107035904

Knowledge management, databases, datamining

Knowledge management, databases, datamining

7

Textbook

Data Mining and Analysis Fundamental Concepts and Algorithms Mohammed J. Zaki Rensselaer Polytechnic Institute, New York

and Wagner Meira, Jr

Mining of Massive Datasets Second edition Jure Leskovec Stanford University, California

Anand Rajaraman Milliways Laboratories

and Jeffrey David Ullman Stanford University, California

Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.

This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

2014 247 x 174 mm 476pp 150 b/w illus. 210 exercises 978-1-107-07723-2 Hardback £45.00 / US$70.00 For all formats available, see

www.cambridge.org/9781107077232

Universidade Federal de Minas Gerais, Brazil

The fundamental algorithms in data mining and analysis are the basis for business intelligence and analytics, automated methods to analyze patterns, and models of data. This textbook for senior undergraduate and graduate data mining courses provides a comprehensive overview from an algorithmic perspective, integrating concepts from machine learning and statistics.

Contents: 1. Data mining and analysis; Part I. Data Analysis Foundations: 2. Numeric attributes; 3. Categorical attributes; 4. Graph data; 5. Kernel methods; 6. High-dimensional data; 7. Dimensionality reduction; Part II. Frequent Pattern Mining: 8. Itemset mining; 9. Summarizing itemsets; 10. Sequence mining; 11. Graph pattern mining; 12. Pattern and rule assessment; Part III. Clustering: 13. Representativebased clustering; 14. Hierarchical clustering; 15. Density-based clustering; 16. Spectral and graph clustering; 17. Clustering validation; Part IV. Classification: 18. Probabilistic classification; 19. Decision tree classifier; 20. Linear discriminant analysis; 21. Support vector machines; 22. Classification assessment.

2014 253 x 177 mm 562pp 186 b/w illus. 85 tables 130 exercises 978-0-521-76633-3 Hardback £40.00 / US$65.00 For all formats available, see

www.cambridge.org/9780521766333

eBooks available at www.cambridge.org/ebookstore

8

Knowledge management, databases, datamining / Computational statistics, machine learning and information science Social Media Mining An Introduction Reza Zafarani Arizona State University

Mohammad Ali Abbasi Arizona State University

and Huan Liu Arizona State University

Social Media Mining integrates social media, social network analysis, and data mining to provide a coherent platform for students, practitioners, researchers and project managers to understand the basics and potentials of social media mining. It presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.

‘This is an exceptionally wellconstructed book on social media that will be useful to academia and industry alike. The book covers the entire area of social network analysis in a comprehensive and understandable way.’

Charu Aggarwal, IBM T. J. Watson Research Center 2014 234 x 156 mm 332pp 109 b/w illus. 20 tables 107 exercises 978-1-107-01885-3 Hardback £40.00 / US$60.00 For all formats available, see

www.cambridge.org/9781107018853

Computational statistics, machine learning and information science An Introduction to Sparse Stochastic Processes Michael Unser École Polytechnique Fédérale de Lausanne

and Pouya D. Tafti École Polytechnique Fédérale de Lausanne

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and sets out a general stochastic framework for developing efficient and practical nonlinear algorithms.

‘Over the last twenty years, sparse representation of images and signals became a very important topic in many applications, ranging from data compression, to biological vision, to medical imaging. The book An Introduction to Sparse Stochastic Processes by Unser and Tafti is the first work to systematically build a coherent framework for non-Gaussian processes with sparse representations by wavelets. Traditional concepts such as Karhunen–Loève analysis of Gaussian processes are nicely complemented by the wavelet analysis of Levy Processes which is

Computational statistics, machine learning and information science / Statistics for econometrics, finance and insurance constructed here. The framework presented here has a classical feel while accommodating the innovative impulses driving research in sparsity.

The book is extremely systematic and at the same time clear and accessible, and can be recommended both to engineers interested in foundations and to mathematicians interested in applications.’

David Donoho, Stanford University 2014 247 x 174 mm 384pp 47 b/w illus. 12 tables 978-1-107-05854-5 Hardback £40.00 / US$65.00 For all formats available, see

9

2014 253 x 177 mm 409pp 47 b/w illus. 123 exercises 978-1-107-05713-5 Hardback £37.50 / US$60.00 For all formats available, see

www.cambridge.org/9781107057135

Statistics for econometrics, finance and insurance

www.cambridge.org/9781107058545

Understanding Machine Learning From Theory to Algorithms Shai Shalev-Shwartz Hebrew University of Jerusalem

and Shai Ben-David University of Waterloo, Ontario

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the ‘hows’ and ‘whys’ of machine-learning algorithms, making the field accessible to both students and practitioners.

‘This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.’

Predictive Modeling Applications in Actuarial Science Volume 1: Predictive Modeling Techniques Edited by Edward W. Frees University of Wisconsin, Madison

Richard A. Derrig Temple University, Philadelphia

and Glenn Meyers ISO Innovative Analytics, New Jersey

This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. It emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts

Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, Germany

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Statistics for econometrics, finance and insurance / Econometrics, statistics and mathematical economics to gain a competitive advantage in situations with complex data.

additional material are available at www.cambridge.org/9781107003675.

International Series on Actuarial Science

Mastering Mathematical Finance

2014 247 x 174 mm 563pp 120 b/w illus. 94 tables 26 exercises 978-1-107-02987-3 Hardback £50.00 / US$85.00

2014 228 x 152 mm 169pp 35 b/w illus. 75 exercises 978-1-107-00367-5 Hardback £50.00 / US$80.00

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978-0-521-17714-6 Paperback £24.00 / US$39.99

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Computation and Modelling in Insurance and Finance Erik Bølviken Universitetet i Oslo

A user-oriented, practical introduction outlining methods for analysing actuarial and financial risk.

International Series on Actuarial Science

2014 247 x 174 mm 709pp 80 b/w illus. 45 tables 550 exercises 978-0-521-83048-5 Hardback £80.00 / US$120.00 For all formats available, see

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Portfolio Theory and Risk Management Maciej J. Capiński AGH University of Science and Technology, Krakow

and Ekkehard Kopp University of Hull

With its focus on examples, exercises and calculations this book suits advanced undergraduates as well as postgraduates and practitioners.

It provides a rigorous treatment of the underlying theory and equips the reader to handle risk assessments in modern finance. Solutions and

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Econometrics, statistics and mathematical economics Applied Choice Analysis Second edition David A. Hensher University of Sydney

John M. Rose University of Sydney

and William H. Greene New York University

A fully updated second edition of this popular book on choice analysis, showing the latest developments in behavioural theory, econometric estimation, and how to apply all methods using the software NLOGIT. Complete with new topics and case studies, this is a valuable resource for

Econometrics, statistics and mathematical economics students, researchers, professionals and consultants.

2015 247 x 174 mm 1196pp 150 b/w illus. 182 tables 978-1-107-09264-8 Hardback £120.00 / US$180.00 978-1-107-46592-3 Paperback £55.00 / US$90.00 Publication May 2015 For all formats available, see

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Textbook

Applied Nonparametric Econometrics Daniel J. Henderson University of Alabama

and Christopher F. Parmeter University of Miami

The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians, discussing basic to advanced nonparametric methods with applications.

Advance praise: ‘A clear and thorough treatment of nonparametric and semiparametric econometrics. The text will be valuable to empirical researchers, who can expand their methodological toolkits without resorting to difficult journal articles. Even advanced topics, such as nonparametric instrumental variables

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and nonparametric models with panel data, are treated at an accessible level.’

Jeffrey M. Wooldridge, Michigan State University

Contents: 1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Inference about the density; 5. Regression; 6. Testing in regression; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference; Bibliography; Index.

2015 253 x 177 mm 462pp 81 b/w illus. 34 tables 978-1-107-01025-3 Hardback £75.00 / US$125.00 978-0-521-27968-0 Paperback £30.00 / US$49.99 Publication March 2015 For all formats available, see

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A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata Subal C. Kumbhakar Binghamton University, State University of New York

Hung-Jen Wang National Taiwan University

and Alan P. Horncastle Oxera Consulting, Oxford

A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata provides practitioners with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach.

Immensely helpful to the applied researcher, it bridges the chasm between theory and practice, expanding the range

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Econometrics, statistics and mathematical economics of applications in which production frontier analysis may be implemented.

Advance praise: ‘A competent empirical application of Stochastic Frontier Analysis (SFA) requires a clear understanding of both the production economics and the econometric theory behind the specified model side by side with adequate programming skills to write the necessary software codes. Apart from a clear exposition of the economic theory behind various stochastic frontier models that represent the technology (like the Distance Functions) and/or producer behavior (like the Cost or Profit Functions) and the relevant econometric theory, the authors offer detailed instructions on how to write the commands for various models in Stata and explain how to interpret the results. This book will prove to be invaluable for every serious researcher using SFA to measure production efficiency.’

Subhash C. Ray, University of Connecticut 2015 228 x 152 mm 476pp 56 b/w illus.

978-1-107-02951-4 Hardback £80.00 / US$125.00 978-1-107-60946-4 Paperback £34.99 / US$49.99 Publication February 2015 For all formats available, see

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Textbook

Dynamic Economic Analysis Deterministic Models in Discrete Time Gerhard Sorger Universität Wien, Austria

For advanced students in economics, this textbook provides a clear and concise introduction to dynamic economic theory and analysis. Sorger guides students step-by-step through the most popular model structures and solution concepts, from the simplest dynamic economic models to complex problems of dynamic general equilibrium frameworks.

‘This book offers a comprehensive vision of economic dynamics suitable for graduate students and professionals alike. Gerhard Sorger is a leading researcher with a flair for presenting mathematically challenging theories carefully and rigorously. His text emphasizes the interplay between formal theory and applications with detailed developments of a catalogue of economic models and examples drawn from macroeconomics, growth theory and game theory. There is no other single book readily accessible in the economics literature covering the same wide range of deterministic dynamics and optimization theories with detailed illustrations of those theories in action. It is accessible to students engaged in a self-study program for students engaging with dynamical systems for the first time.

Better yet, it offers the topics and treatments for a course in dynamics.’

Robert A. Becker, Indiana University, Bloomington

Econometrics, statistics and mathematical economics Contents: Preface; Part I. Difference Equations: 1. Basic concepts; 2. Linear difference equations; 3. Autonomous difference equations; 4. One-dimensional maps; Part II. Dynamic Optimization: 5. Optimization techniques; 6. Dynamic inconsistency and commitment; 7. Dynamic games; 8. Dynamic competitive equilibrium; References; Index.

2014 228 x 152 mm 143pp 9 b/w illus. 18 tables 978-1-107-08159-8 Hardback £45.00 / US$70.00

2015 246 x 189 mm 301pp 30 b/w illus.

978-1-107-08329-5 Hardback £65.00 / US$99.00

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Expert Adjustments of Model Forecasts Theory, Practice and Strategies for Improvement Philip Hans Franses Erasmus Universiteit Rotterdam

Written for academics and practitioners with an interest in forecasting methodology, this book tests the notion that many forecasters feel they can improve the accuracy of forecasts based on their intuition. Current research is collated to examine ‘expert adjustment’ from an econometric perspective, and guidelines for improvement are suggested.

‘All economic and business forecasting involves judgment to some extent, but most books focus on the application of statistical methods. At last we have a book that directly addresses the role of judgment in forecasting and applies a set of rigorous methods to assess its potential strengths and limitations.’

Paul Goodwin, Emeritus Professor, University of Bath

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978-1-107-44161-3 Paperback £17.99 / US$27.99 For all formats available, see

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Time Series Models for Business and Economic Forecasting Second edition Philip Hans Franses Erasmus School of Economics

Dick van Dijk Erasmus School of Economics

and Anne Opschoor Erasmus School of Economics

Taking a practical approach, this updated and classroom-tested textbook prepares students to create effective forecasting models for business and economics.

Review of the first edition: ‘Reviews the more recent developments in modeling time series to focus on generating ex ante forecasts: seasonal unit roots, period models, aberrant observations, and common features. For each method, intuitive motivation and practical considerations are discussed in detail, making the book very readable … should be beneficial for students and instructors of applications-oriented courses as well as for practitioners who wish to obtain a first, but not too technical, impression of time series forecasting using modern, recently developed methods.’

Journal of the American Statistical Association

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Econometrics, statistics and mathematical economics Contents: Preface; 1. Introduction and overview; 2. Key features of economic time series; 3. Useful concepts in univariate time series analysis; 4. Trends; 5. Seasonality; 6. Aberrant observations; 7. Conditional heteroskedasticity; 8. Non-linearity; 9. Multivariate time series; Index.

2014 246 x 189 mm 314pp 81 b/w illus. 17 tables 65 exercises 978-0-521-81770-7 Hardback £75.00 / US$120.00 978-0-521-52091-1 Paperback £30.00 / US$48.00 For all formats available, see

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Analysis of Panel Data Third edition Cheng Hsiao University of Southern California

This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling crosssectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined.

Contents: 1. Introduction; 2. Homogeneity test for linear regression models (analysis of covariance); 3. Simple regression with variable intercepts; 4. Dynamic models with variable intercepts; 5. Simultaneousequations models; 6. Variable-coefficient models; 7. Discrete data; 8. Truncated

and censored data; 9. Cross-sectional dependent panel data; 10. Dynamic system; 11. Incomplete panel data; 12. Miscellaneous topics; 13. A summary view.

Econometric Society Monographs, 54

2015 228 x 152 mm 455pp 7 b/w illus. 9 tables 978-1-107-03869-1 Hardback £75.00 / US$125.00 978-1-107-65763-2 Paperback £32.99 / US$49.99 For all formats available, see

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Almost All about Unit Roots Foundations, Developments, and Applications In Choi Sogang University, Seoul

Many economic theories depend on the presence or absence of a unit root for their validity, making familiarity with unit roots extremely important to econometric and statistical theory. This book introduces the literature on unit roots in a comprehensive manner to

Econometrics, statistics and mathematical economics / Mathematical modelling and methods / Probability theory and stochastic processes empirical and theoretical researchers in economics and other areas.

Contents: 1. Introduction; 2. Inference on unit roots: basic methods; 3. Unit root tests under various model specifications; 4. Alternative approaches to inference on unit roots; 5. Other issues related to unit roots; 6. Seasonal unit roots; 7. Panel unit roots.

Themes in Modern Econometrics

2015 228 x 152 mm 220pp 27 tables 978-1-107-09733-9 Hardback £60.00 / US$95.00 978-1-107-48250-0 Paperback £24.99 / US$39.99 Publication May 2015 For all formats available, see

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Mathematical modelling and methods Methods of Applied Mathematics for Engineers and Scientists Tomas B. Co Michigan Technological University

This engineering mathematics textbook is rich with examples, applications and exercises, and emphasises applying matrices.

2013 253 x 177 mm 582pp 176 b/w illus. 52 tables 298 exercises 978-1-107-00412-2 Hardback £75.00 / US$135.00

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Probability theory and stochastic processes The Cambridge Dictionary of Probability and Its Applications David Stirzaker St John’s College, Oxford

Probability comes of age with this, the first dictionary of probability and its applications in English, which supplies a guide to the concepts and vocabulary of this rapidly expanding field. Besides the basic theory of probability and random processes, applications covered here include financial and insurance mathematics, operations research (including queueing, reliability, and inventories), decision and game theory, optimization, time series, networks, and communication theory, as well as classic problems and paradoxes. The dictionary is reliable, stable, concise, and cohesive. Each entry provides a rigorous definition, a sketch of the context, and a reference pointing the reader to the wider literature. Judicious use of figures makes complex concepts easier to follow without oversimplifying. As the only dictionary on the market, this will be a guiding reference for all those working

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Probability theory and stochastic processes in, or learning, probability together with its applications.

2015 246 x 189 mm 500pp 80 b/w illus.

978-1-107-07516-0 Hardback c. £120.00 / c. US$199.00 Publication June 2015 For all formats available, see

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Probability: The Classical Limit Theorems Henry McKean New York University

Probability theory is explained here by one of its leading authorities. McKean constructs a clear path through the subject and sheds light on a variety of interesting topics in which probability theory plays a key role. Anyone who wants to learn or use probability will benefit from reading this book.

2014 228 x 152 mm 485pp 122 b/w illus. 260 exercises 978-1-107-05321-2 Hardback £60.00 / US$99.00 978-1-107-62827-4 Paperback £27.99 / US$45.99 For all formats available, see

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Probability and Statistics by Example Volume 1: Basic Probability and Statistics Second edition Yuri Suhov University of Cambridge

and Mark Kelbert Swansea University

A valuable resource for students and teachers alike, this second edition is packed with over 200 worked examples and exam questions with solutions.

It promotes a deep understanding of the subject rather than a superficial knowledge of the theory, equipping students to solve problems in practice and under exam conditions.

Review of previous edition: ‘… all the ingredients that contribute to making a good lecture are in the book: well-explained theory, interesting examples, and funny jokes and amusing stories about famous probabilists and statisticians. The authors also give rhythm to the flow of the subjects, making the volume a very pleasant book to read.’

Emanuele Taufer, Mathematical Reviews 2014 247 x 174 mm 470pp 40 b/w illus. 210 exercises 978-1-107-60358-5 Paperback £45.00 / US$75.00 For all formats available, see

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Quantum Stochastics Mou-Hsiung Chang Mathematical Sciences Division, US Army Research Office

This book provides a systematic, self-contained treatment of the theory of quantum probability and quantum Markov processes for graduate students and researchers. Building a framework that parallels the development of classical probability, it aims to help readers up the steep learning curve of the quantum theory.

‘This excellent introductory book on quantum stochastics is most timely – the highly interdisciplinary book needed in this century. Dr Chang has organized and presented the material in a systematic and coherent manner … [The] material is self-contained and self-readable; ideas, concepts and

Probability theory and stochastic processes topics are up-to-date, well-motivated, well presented and well associated with the classical approach: starting from the idea of a quantum system, to quantum probability space, measure, processes with quantum stochastic calculus, quantum stochastic differential equations, quantum stability, quantum semigroups, and so on. I am looking forward to using this book for my graduate course on quantum stochastic dynamic systems.

I will also highly recommend it to my students and colleagues.’

Stochastic Equations in Infinite Dimensions

G. S. Ladde, University of South Florida, and founder and editor of the Journal of Stochastic Analysis and Applications

2014 234 x 156 mm 512pp 978-1-107-05584-1 Hardback £80.00 / US$125.00

Cambridge Series in Statistical and Probabilistic Mathematics, 37

2015 253 x 177 mm 372pp 978-1-107-06919-0 Hardback £55.00 / US$85.00 For all formats available, see

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Uniform Central Limit Theorems Second edition R. M. Dudley Massachusetts Institute of Technology

This expanded edition of the classic work on empirical processes now boasts several new proved theorems not in the first.

Cambridge Studies in Advanced Mathematics, 142

2014 228 x 152 mm 486pp 978-0-521-49884-5 Hardback £70.00 / US$120.00 978-0-521-73841-5 Paperback £29.99 / US$50.00 For all formats available, see

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Second edition Giuseppe Da Prato Scuola Normale Superiore, Pisa

and Jerzy Zabczyk Polish Academy of Sciences

Updates in this second edition include two brand new chapters and an even more comprehensive bibliography.

Encyclopedia of Mathematics and its Applications, 152

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The Surprising Mathematics of Longest Increasing Subsequences Dan Romik University of California, Davis

This book presents for the first time to a graduate-level readership recent groundbreaking developments in probability and combinatorics related to the longest increasing subsequence problem. Its detailed, playful presentation provides a motivating entry to elegant mathematical ideas that are of interest to every mathematician and to many computer scientists, physicists and statisticians.

Advance praise: ‘The story of longest monotone subsequences in permutations has been, for six decades, one of the most beautiful in mathematics, ranging from the very pure to the applied and featuring many terrific mathematicians, starting with Erdős–

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Probability theory and stochastic processes Szekeres’s ‘happy end theorem’ and continuing through the Tracy–Widom distribution and the breakthrough of Baik–Deift–Johansson. With its connections to many areas of mathematics, to the Riemann hypothesis, and to high-energy physics we cannot foresee where the story is heading. Dan Romik tells the tale thus far – and teaches its rich multifaceted mathematics, a blend of probability, combinatorics, analysis, and algebra – in a wonderful way.’

Gil Kalai, Hebrew University Institute of Mathematical Statistics Textbooks, 4

2015 228 x 152 mm 432pp 3 b/w illus. 94 exercises 978-1-107-07583-2 Hardback £65.00 / US$99.00 978-1-107-42882-9 Paperback £27.99 / US$39.99 Publication March 2015 For all formats available, see

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probability theory and integration theory.

Each chapter ends with exercises.

Advance praise: ‘Presented in an orderly, accessible manner, this book elegantly integrates the general theory of noise sensitivity into a concrete study, allowing for a new understanding of the percolation process. It provides an excellent and clear exposition of the general theory of noise sensitivity and its manifestation in two-dimensional critical percolation.’

Itai Benjamini, Weizmann Institute of Science, Israel Institute of Mathematical Statistics Textbooks, 5

2015 228 x 152 mm 222pp 29 b/w illus. 75 exercises 978-1-107-07643-3 Hardback £60.00 / US$99.00 978-1-107-43255-0 Paperback £21.99 / US$34.99 Publication February 2015 For all formats available, see

Noise Sensitivity of Boolean Functions and Percolation Christophe Garban Ecole Normale Supérieure, Lyon

and Jeffrey E. Steif Chalmers University of Technology, Gothenberg

This account of the new and exciting area of noise sensitivity of Boolean functions – in particular applied to critical percolation – is designed for graduate students and researchers in probability theory, discrete mathematics, and theoretical computer science.

It assumes a basic background in

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Random Matrix Theory, Interacting Particle Systems, and Integrable Systems Edited by Percy Deift New York University, Courant Institute of Mathematical Sciences

and Peter Forrester University of Melbourne

This volume, based on the Fall 2010 MSRI program, includes review articles, research contributions on long-standing questions on universalities of Wigner matrices and beta-ensembles, and other core aspects of random matrix theory

Probability theory and stochastic processes / Applied probability and stochastic networks such as integrability and free probability theory.

Mathematical Sciences Research Institute Publications, 65

2015 234 x 156 mm 552pp 978-1-107-07992-2 Hardback £75.00 / US$120.00 Publication February 2015 For all formats available, see

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Applied probability and stochastic networks

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Markov Chains and Dependability Theory Gerardo Rubino IRISA, Rennes, France

and Bruno Sericola IRISA, Rennes, France

Dependability metrics are omnipresent in every engineering field, from simple ones through to more complex measures combining performance and dependability aspects of systems. This book presents the mathematical basis of the analysis of these metrics using Markov models, and is of interest to graduate students and researchers.

2014 247 x 174 mm 284pp 32 b/w illus. 24 tables 978-1-107-00757-4 Hardback £65.00 / US$99.00 For all formats available, see

Probability for Statistical Science Joseph Blitzstein Harvard University, Massachusetts

and Carl Morris Harvard University, Massachusetts

This graduate text arises from Harvard University’s celebrated Stat 110 course and covers standard material from the point of view of what is needed for the data sciences. It introduces ideas from measure theory, but with a light hand and fully motivated; the aim is to equip students to understand constructions that use measure theory.

2015 253 x 177 mm 400pp 978-0-521-76634-0 Hardback c. £75.00 / c. US$120.00 Publication November 2015

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An Introduction to Computational Stochastic PDEs Gabriel J. Lord Heriot-Watt University, Edinburgh

Catherine E. Powell University of Manchester

and Tony Shardlow University of Bath

This comprehensive introduction to stochastic partial differential equations incorporates the effects of randomness into real-world models, offering graduate students and researchers powerful tools for understanding uncertainty quantification for risk analysis. MATLAB codes are included, so that readers can

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Applied probability and stochastic networks / Optimization, OR and risk analysis perform computations themselves and solve the test problems discussed.

optimization and motivate the theory with real-world examples and exercises.

Cambridge Texts in Applied Mathematics, 50

Contents: Preface; 1. Introduction; 2. Solving linear programs; 3. Duality through examples; 4. Duality theory; 5. Applications of duality; 6. Solving integer programs; 7. Nonlinear optimization; Appendix A. Computational complexity; References; Index.

2014 247 x 174 mm 516pp 107 b/w illus. 16 colour illus. 222 exercises 978-0-521-89990-1 Hardback £80.00 / US$130.00 978-0-521-72852-2 Paperback £37.50 / US$60.00 For all formats available, see

www.cambridge.org/9780521899901

Optimization, OR and risk analysis

2014 247 x 174 mm 279pp 55 b/w illus. 25 colour illus. 20 tables 140 exercises 978-1-107-05344-1 Hardback £50.00 / US$80.00 978-1-107-65879-0 Paperback £25.00 / US$40.00 For all formats available, see

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A Gentle Introduction to Optimization

An Introduction to Polynomial and Semi-Algebraic Optimization

B. Guenin

Jean Bernard Lasserre

Textbook

University of Waterloo, Ontario

J. Könemann University of Waterloo, Ontario

and L. Tunçel University of Waterloo, Ontario

Assuming only basic linear algebra, this textbook is the perfect starting point for a wide range of undergraduate students from across the mathematical sciences (computer science, engineering, economics, and so on). The authors focus on the fundamental ideas in

Centre National de la Recherche Scientifique (CNRS), Toulouse

This is the first comprehensive introduction to the powerful moment approach for solving global optimization problems. Graduate students, engineers and researchers entering the field can use this book to understand, experiment with and master this new approach through the simple worked examples provided.

Optimization, OR and risk analysis / Also of interest Cambridge Texts in Applied Mathematics, 52

2015 228 x 152 mm 370pp 15 b/w illus. 2 colour illus.

978-1-107-06057-9 Hardback £70.00 / US$120.00 978-1-107-63069-7 Paperback £35.00 / US$60.00 Publication February 2015 For all formats available, see

www.cambridge.org/9781107060579

Also of interest The Art of Electronics Third edition Paul Horowitz Harvard University, Massachusetts

and Winfield Hill

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know you didn’t know. Or, refer to the pinouts, diagrams, and techniques as necessary to guide you through a difficult project. If you think electrical engineering is magical then you must pick up this tome!’

Limor ‘Ladyada’ Fried, Adafruit Industries 2015 253 x 203 mm 1224pp 1470 b/w illus. 78 tables 978-0-521-80926-9 Hardback £59.99 / US$120.00 Publication June 2015 For all formats available, see

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Optimization Models Giuseppe C. Calafiore Politecnico di Torino

Rowland Institute of Science, Harvard University, Massachusetts

and Laurent El Ghaoui

At long last, here is the thoroughly revised and updated, and longanticipated, third edition of the hugely successful The Art of Electronics. Widely accepted as the best single authoritative text on electronic circuit design, it will be an indispensable reference and the gold standard for anyone in the field.

Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook teaches students how to recognize, simplify, model and solve optimization problems – and apply these basic principles to their own projects. Accompanied by an online solution manual, accessible only to instructors.

Advance praise: ‘Who among us has not kept a cherished copy of AoE on our workbench throughout our careers?

Engineers, hackers and makers of all stripes, rejoice for the third edition … has been worth the wait! Packed with tons of delicious knowledge to navigate electronics in both work and hobby. An encyclopedia of electronics knowledge, [The Art of Electronics] is a pleasure to read through for tips and tricks and is an unbeatable resource!

Take a day out to read a chapter – you will learn things you didn’t even

‘In Optimization Models, Calafiore and El Ghaoui have created a beautiful and very much needed on-ramp to the world of modern mathematical optimization and its wide range of applications. They lead an undergraduate, with not much more than basic calculus behind her, from the basics of linear algebra all the way to modern optimization-based machine learning, image processing, control, and finance, to name just a few applications. Until now, these methods and topics were accessible

University of California, Berkeley

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22

Also of interest only to graduate students in a few fields, and the few undergraduates who brave the daunting prerequisites.

The book’s seamless integration of mathematics and applications, and its focus on modeling practical problems and algorithmic solution methods, will be very appealing to a wide audience.’

Stephen Boyd, Stanford University

Contents: 1. Introduction; Part I. Linear Algebra: 2. Vectors; 3. Matrices; 4. Symmetric matrices; 5. Singular value decomposition; 6. Linear equations and least-squares; 7. Matrix algorithms; Part II. Convex Optimization: 8. Convexity; 9. Linear, quadratic and geometric models; 10. Second-order cone and robust models; 11. Semidefinite models; 12. Introduction to algorithms; Part III. Applications: 13. Learning from data; 14. Computational finance; 15. Control problems; 16. Engineering design.

2014 246 x 189 mm 650pp 352 b/w illus. 126 exercises 978-1-107-05087-7 Hardback £48.00 / US$69.99

for design professionals and is a resource for educators.

2014 253 x 177 mm 350pp 88 b/w illus. 48 tables 90 exercises 978-1-107-03874-5 Hardback £85.00 / US$145.00 For all formats available, see

www.cambridge.org/9781107038745

Textbook

Bayesian Cognitive Modeling A Practical Course Michael D. Lee University of California, Irvine

and Eric-Jan Wagenmakers Universiteit van Amsterdam

Using a practical, hands-on approach, this book will teach anyone how to carry out Bayesian analyses and interpret the results.

For all formats available, see

‘This book provides the best practical guide to date on how to do Bayesian modeling in cognitive science.’

www.cambridge.org/9781107050877

Jay Myung, Ohio State University

Probability-Based Structural Fire Load Leo Razdolsky LR Structural Engineering, Inc., Chicago

Probability-Based Structural Fire Load introduces the subject of probabilistic analysis to engineers and can also act as a reference to guide those applying this technology. In addition to providing an understanding of how fire affects structures, this book provides guidance

Contents: Part I. Getting Started: 1. The basics of Bayesian analysis; 2. Getting started with WinBUGS; Part II. Parameter Estimation: 3. Inferences with binomials; 4. Inferences with Gaussians; 5. Some examples of data analysis; 6. Latent mixture models; Part III. Model Selection: 7. Bayesian model comparison; 8. Comparing Gaussian means; 9. Comparing binomial rates; Part IV. Case Studies: 10. Memory retention; 11. Signal detection theory; 12. Psychophysical functions; 13. Extrasensory perception;

Also of interest 14. Multinomial processing trees; 15. The SIMPLE model of memory; 16. The BART model of risk taking; 17. The GCM model of categorization; 18. Heuristic decisionmaking; 19. Number concept development.

autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.

2014 246 x 189 mm 280pp 978-1-107-01845-7 Hardback £65.00 / US$110.00

2015 228 x 152 mm 272pp 93 b/w illus. 30 tables 978-0-521-87116-7 Hardback £55.00 / US$95.00

978-1-107-60357-8 Paperback £27.99 / US$45.00 For all formats available, see

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The Art of Lecturing A Practical Guide to Successful University Lectures and Business Presentations Parham Aarabi University of Toronto

An accessible, example-based guide to effective strategies for successful academic and business lectures.

2007 228 x 152 mm 166pp 978-0-521-70352-9 Paperback £17.99 / US$29.99 For all formats available, see

www.cambridge.org/9780521703529

Time Series Analysis for the Social Sciences Janet M. Box-Steffensmeier Ohio State University

John R. Freeman University of Minnesota

Matthew P. Hitt Louisiana State University

and Jon C. W. Pevehouse University of Wisconsin, Madison

Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector

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Analytical Methods for Social Research

978-0-521-69155-0 Paperback £19.99 / US$34.99 Publication February 2015 For all formats available, see

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Textbook

Statistical Modeling and Inference for Social Science Sean Gailmard University of California, Berkeley

Written specifically for graduate students and practitioners beginning social science research, this textbook introduces the essential statistical tools, models and theories that make up the social scientist’s toolkit. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard demonstrates how social scientists assess relationships between variables.

‘With careful consideration for both rigor and intuition, Gailmard fills a large void in the social science literature. Those seeking clear mathematical exposition will not be disappointed. Those hoping for substantive applications to illuminate

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Also of interest the data analysis will also be pleased.

This book strikes a nearly perfect balance.’

students using mixed-methods research to study causal mechanisms.

Wendy K. Tam Cho, National Center for Supercomputing Applications and University of Illinois, Urbana-Champaign

‘Finding Pathways makes an important contribution to the growing methodological literature on the identification of causal mechanisms.

Weller and Barnes provide researchers a powerful set of tools to investigate causal processes by showing how the logic of causal mediation analysis can bridge quantitative and qualitative research.’

Contents: 1. Introduction; 2. Descriptive statistics: data and information; 3. Observable data and data-generating processes; 4. Probability theory: basic properties of data-generating processes; 5. Expectation and moments: summaries of data-generating processes; 6. Probability and models: linking positive theories and data-generating processes; 7. Sampling distributions: linking data-generating processes and observable data; 8. Hypothesis testing: assessing claims about the data-generating process; 9. Estimation: recovering properties of the data-generating process; 10. Causal inference: inferring causation from correlation; Afterword: statistical methods and empirical research.

Analytical Methods for Social Research

2014 228 x 152 mm 388pp 18 b/w illus. 18 tables 978-1-107-00314-9 Hardback £45.00 / US$75.00

Kosuke Imai, Princeton University, New Jersey Strategies for Social Inquiry

2014 247 x 174 mm 171pp 16 b/w illus. 14 tables 978-1-107-04106-6 Hardback £55.00 / US$85.00 978-1-107-68476-8 Paperback £21.99 / US$32.99 For all formats available, see

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Mixed Methods Social Networks Research Design and Applications Edited by Silvia Domínguez

For all formats available, see

Northeastern University, Boston

www.cambridge.org/9781107003149

and Betina Hollstein Universität Bremen

Finding Pathways Mixed-Method Research for Studying Causal Mechanisms Nicholas Weller University of Southern California

and Jeb Barnes University of Southern California

This book introduces ‘pathway analysis’: a systematic technique for case selection for small-N research and a means to relate small and large-N research to each other. This approach will be invaluable for researchers and graduate

This volume demonstrates the potential of mixed-methods designs for researching social networks and the utilization of social networks for other research. Mixing methods applies to the combination and integration of qualitative and quantitative methods.

In social network research, mixing methods also applies to the combination

Also of interest

25

of structural and actor-oriented approaches.

‘In Mixed Methods Social Networks Research: Design and Applications, Silvia Domínguez and Betina Hollstein provide an inspiring volume with offerings that embrace disciplines and methods from across the social sciences. Its chapters highlight the rich insights that can be gained by adopting fully integrated mixedmethod approaches for studying social networks. This transformative book is certain to stimulate a new generation of social network researchers who are comfortable with both ethnographic methods and mathematical models.

A very welcome addition to the bookshelf of social network methods texts!’

Katherine Faust, University of California, Irvine Structural Analysis in the Social Sciences, 36

2014 228 x 152 mm 404pp 54 b/w illus. 29 tables 978-1-107-02792-3 Hardback £65.00 / US$110.00 978-1-107-63105-2 Paperback £24.99 / US$39.99 For all formats available, see

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Index A Aarabi, Parham......................................23 Abbasi, Mohammad Ali............................8 Almost All about Unit Roots....................14 Analysis of Panel Data............................14 Applied Choice Analysis..........................10 Applied Nonparametric Econometrics......11 Art of Electronics, The.............................21 Art of Lecturing, The...............................23

B Bai, Zhidong.............................................4 Barnes, Jeb.............................................24 Bate, Simon T............................................6 Bayesian Cognitive Modeling..................22 Bayesian Probability Theory......................6 Ben-David, Shai........................................9 Bender, Stefan..........................................2 Blitzstein, Joseph....................................19 Bookstein, Fred L......................................5 Box-Steffensmeier, Janet M.....................23 Bølviken, Erik.........................................10

C Calafiore, Giuseppe C.............................21 Cambridge Dictionary of Probability and Its Applications, The.............................15 Capiński, Maciej J...................................10 Case-Control Studies................................4 Causal Inference for Statistics, Social, and Biomedical Sciences........................1 Chang, Mou-Hsiung...............................16 Choi, In..................................................14 Clark, Robin A..........................................6 Co, Tomas B...........................................15 Computation and Modelling in Insurance and Finance.........................10 Confidence, Likelihood and Probability......1 Core Statistics..........................................4 Counterfactuals and Causal Inference.......3 Cox, D. R..................................................4

D Da Prato, Giuseppe.................................17

Data Mining and Analysis.........................7 Deift, Percy.............................................18 Derrig, Richard A......................................9 Design and Statistical Analysis of Animal Experiments, The....................................6 Dijk, Dick van.........................................13 Domínguez, Silvia...................................25 Dose, Volker.............................................6 Dudley, R. M...........................................17 Dynamic Economic Analysis....................12

E El Ghaoui, Laurent..................................21 Eldar, Yonina C..........................................6 Expert Adjustments of Model Forecasts...13

F Finding Pathways...................................24 Forrester, Peter.......................................18 Franses, Philip Hans................................13 Freeman, John R.....................................23 Frees, Edward W.......................................9

G Gailmard, Sean.......................................23 Garban, Christophe................................18 Gentle Introduction to Optimization, A....20 Ghosh, Debashis......................................5 Greene, William H..................................10 Groeneboom, Piet....................................3 Guenin, B...............................................20

H Henderson, Daniel J................................11 Hensher, David A....................................10 Hilbe, Joseph M........................................2 Hill, Winfield..........................................21 Hitt, Matthew P......................................23 Hjort, Nils Lid...........................................1 Hollstein, Betina.....................................25 Horncastle, Alan P...................................11 Horowitz, Paul........................................21 Hsiao, Cheng..........................................14

Index I Imbens, Guido W......................................1 Integrating Omics Data.............................5 Introduction to Computational Stochastic PDEs, An.............................19 Introduction to Polynomial and SemiAlgebraic Optimization, An..................20 Introduction to Sparse Stochastic Processes, An........................................8

J Jongbloed, Geurt......................................3

K Kelbert, Mark.........................................16 Keogh, Ruth H..........................................4 Könemann, J..........................................20 Kopp, Ekkehard......................................10 Kumbhakar, Subal C................................11

L Lane, Julia................................................2 Large Sample Covariance Matrices and High-Dimensional Data Analysis.............4 Lasserre, Jean Bernard............................20 Lee, Michael D........................................22 Leskovec, Jure..........................................7 Linden, Wolfgang von der.........................6 Liu, Huan.................................................8 Lord, Gabriel J........................................19

M Markov Chains and Dependability Theory.................................................19 McKean, Henry.......................................16 Measuring and Reasoning........................5 Meira, Jr, Wagner.....................................7 Methods of Applied Mathematics for Engineers and Scientists......................15 Meyers, Glenn..........................................9 Mining of Massive Datasets......................7 Mixed Methods Social Networks Research.............................................25 Modeling Count Data...............................2

27

Morgan, Stephen L...................................3 Morris, Carl............................................19

N Nissenbaum, Helen..................................2 Noise Sensitivity of Boolean Functions and Percolation...................................18 Nonparametric Estimation under Shape Constraints............................................3

O Opschoor, Anne......................................13 Optimization Models..............................21

P Parmeter, Christopher F...........................11 Pevehouse, Jon C. W...............................23 Portfolio Theory and Risk Management...10 Powell, Catherine E................................19 Practitioner’s Guide to Stochastic Frontier Analysis Using Stata, A............11 Predictive Modeling Applications in Actuarial Science...................................9 Privacy, Big Data, and the Public Good......2 Probability and Statistics by Example......16 Probability for Statistical Science.............19 Probability-Based Structural Fire Load.....22 Probability: The Classical Limit Theorems.16

Q Quantum Stochastics..............................16

R Rajaraman, Anand....................................7 Random Matrix Theory, Interacting Particle Systems, and Integrable Systems...............................................18 Razdolsky, Leo........................................22 Romik, Dan............................................17 Rose, John M..........................................10 Rubin, Donald B.......................................1 Rubino, Gerardo.....................................19

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Index S Sampling Theory.......................................6 Schweder, Tore.........................................1 Sericola, Bruno.......................................19 Shalev-Shwartz, Shai................................9 Shardlow, Tony.......................................19 Social Media Mining.................................8 Sorger, Gerhard......................................12 Statistical Modeling and Inference for Social Science......................................23 Steif, Jeffrey E.........................................18 Stirzaker, David.......................................15 Stochastic Equations in Infinite Dimensions.........................................17 Stodden, Victoria......................................2 Suhov, Yuri.............................................16 Surprising Mathematics of Longest Increasing Subsequences, The..............17

T Tafti, Pouya D...........................................8 Time Series Analysis for the Social Sciences..............................................23 Time Series Models for Business and Economic Forecasting..........................13 Toussaint, Udo von...................................6

Tseng, George C.......................................5 Tunçel, L.................................................20

U Ullman, Jeffrey David................................7 Understanding Machine Learning.............9 Uniform Central Limit Theorems..............17 Unser, Michael.........................................8

W Wagenmakers, Eric-Jan...........................22 Wang, Hung-Jen.....................................11 Weller, Nicholas......................................24 Winship, Christopher................................3 Wood, Simon...........................................4

Y Yao, Jianfeng............................................4

Z Zabczyk, Jerzy........................................17 Zafarani, Reza..........................................8 Zaki, Mohammed J...................................7 Zheng, Shurong........................................4 Zhou, Xianghong Jasmine.........................5

Notes

29

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