Cambridge Series in Statistical and Probabilistic Mathematics High-Dimensional Statistics
Predictive Statistics
A Non-Asymptotic Viewpoint
Analysis and Inference beyond Models
Martin J. Wainwright
Bertrand S. Clarke and Jennifer L. Clarke
University of California, Berkeley
Recent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data. ‘Non-asymptotic, high-dimensional theory is critical for modern statistics and machine learning. This book is unique in providing a crystal clear, complete and unified treatment of the area. With topics ranging from concentration of measure to graphical models, the author weaves together probability theory and its applications to statistics. Ideal for graduate students and researchers. This will surely be the standard reference on the topic for many years.’ Larry Wasserman, Carnegie Mellon University, Pennsylvania ‘Martin J. Wainwright brings his large box of analytical power tools to bear on the problems of the day – the analysis of models for wide data. A broad knowledge of this new area combines with his powerful analytical skills to deliver this impressive and intimidating work – bound to be an essential reference for all the brave souls that try their hand.’ Trevor Hastie, Stanford University, California Cambridge Series in Statistical and Probabilistic Mathematics, 48
2019 253 x 177 mm 568pp 49 b/w illus. 1 table 211 exercises 978-1-108-49802-9 Hardback £57.99 / US$79.99 For all formats available, see
www.cambridge.org/9781108498029
University of Nebraska, Lincoln
Aimed at statisticians and machine learners, this retooling of statistical theory asserts that high-quality prediction should be the guiding principle of modeling and learning from data, then shows how. The fully predictive approach to statistical problems outlined embraces traditional subfields and ‘black box’ settings, with computed examples. Contents: Part I. The Predictive View; Part II. Established Settings for Prediction; Part III. Contemporary Prediction. ‘Prediction, one of the most important practical applications of statistical analysis, has rarely been treated as anything more than an afterthought in most formal treatments of statistical inference. This important book aims to counter this neglect by a wholehearted emphasis on prediction as the primary purpose of the analysis. The authors cut a broad swathe through the statistical landscape, conducting thorough analyses of numerous traditional, recent, and novel techniques, to show how these are illuminated by taking the predictive perspective.’ Philip Dawid, University of Cambridge ‘The prime focus in statistics has always been on modeling rather than prediction; as a result, different prediction methods have arisen within different subfields of statistics, and a general, all-encompassing account has been lacking. For the first time, this book provides such an account and, as such, it convincingly argues for the primacy of prediction. The authors consider a wide range of topics from a predictive point of view and I am impressed by both the breadth and depth of the topics addressed and by the unifying story the authors manage to tell.’ Peter Grünwald, Centrum Wiskunde & Informatica and Universiteit Leiden Cambridge Series in Statistical and Probabilistic Mathematics, 46
2018 253 x 177 mm 656pp 978-1-107-02828-9 Hardback £64.99 / US$84.99 For all formats available, see
www.cambridge.org/9781107028289
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