Pioneers Plotting the Future
SATURDAY, JULY 31 Monte Carlo and Bayesian Computation with R (CE_03C) Section for Statistical Programmers and Analysts, Section on Bayesian Statistical Science 4 Maria Rizzo, author of Statistical Computing with R teaches this course with Jim Albert, co-editor of Statistical Thinking in Sports. Causal Inference (CE_01C) 4 Miguel Hernan and James Robins, co-authors of the forthcoming Causal Inference, teach this course. SUNDAY, AUGUST 1 Statistical Issues in Approval of Follow-on Biologics - Invited – Papers 47 Biopharmaceutical Section, ENAR 4 Shein-Chung Chow, series editor for Chapman & Hall/CRC Biostatistics Series, chairs this session. Bayesian Ecology: Hierarchical Modeling for Ecological Processes (CE_10C) 4 Instructed by Alan E. Gelfand, co-author of Handbook of Spatial Statistics and Hierarchical Modeling and Analysis for Spatial Data. Statistical Methods for Spatial Longitudinal/Functional Data Invited – Papers 52 Section on Statistical Computing, IMS, International Chinese Statistical Association, Section on Nonparametric Statistics, Section on Physical and Engineering Sciences, Section on Statistics and the Environment, WNAR 4 Sudipto Banerjee, co author of Hierarchical Modeling and Analysis for Spatial Data and Linear Algebra and Matrix Computations for Statistics, presents Hierarchical Spatial Models for Predicting Forest Variables over Large Heterogeneous Domains with Andrew Finley. MONDAY, AUGUST 2 Medallion Lecture - Invited – Papers 146 IMS, International Chinese Statistical Association 4 Xiao-Li Meng, co-editor of Handbook of Markov Chain Monte Carlo: Methods and Applications, presents What Can We Do When EM Is Not Applicable? Self Consistency: A General Recipe for Semi-parametric and Non-parametric Estimation with Incomplete and Irregularly Spaced Data. JASA, Theory and Methods Invited Session - Invited – Papers 152 JASA, Theory and Methods 4 Bradley Efron, co-author of An Introduction to the Bootstrap, presents Correlated z-values and the Accuracy of Large-scale Statistical Estimates. TUESDAY, AUGUST 3 Analysis of Longitudinal Data Using Antedependence Models (CE_17C) 4 Instructed by Dale Zimmerman, co-author of Antedependence Models for Longitudinal Data. Bayesian Adaptive Methods for Clinical Trials (CE_19C) 4 Taught by Bradley P. Carlin, Scott Berry, and J. Jack Lee, coauthors of Bayesian Adaptive Methods for Clinical Trials, with Donald Berry, co-authors of Bayesian Biostatistics
Advances in Functional Data Analysis - Invited – Papers 272 Section on Nonparametric Statistics 4 Raymond Carroll, co-author of Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition presents Generalized Functional Latent Feature Models with Single-Index Interactions with Yehua Li and Naisyin Wang. His co-author Ciprian Crainiceanu presents Longitudinal Functional Principal Component Analysis. Statistical Analysis of Complex Networks - a SAMSI Preview Invited – Papers 384 Section on Statistical Computing 4 Mike West, author of Time Series: Modeling, Computation, and Inference examines Issues in Model Emulation/Evaluation in Dynamic Network Studies in Systems Biology. Statistical Methods Used in Defense and Non-defense Applications - Invited – Panel 326 Section on Statistics in Defense and National Security, International Chinese Statistical Association 4 Max Morris, author of Design of Experiments: An Introduction Based on Linear Models presents Statistical Methods Used in Defense and Non-defense Applications with collaborators. WEDNESDAY, AUGUST 4 Graphics Packages for R, Recent Advances and Future Directions - Invited – Papers 446 Section on Statistical Graphics, Committee on Applied Statisticians, Section for Statistical Programmers and Analysts, Section on Government Statistics, Section on Statistical Computing 4 Organized and chaired by Daniel B. Carr, co-author of Visualizing Data Patterns with Micromaps. Section on Survey Research Methods PM Roundtable Discussion (fee event) 549 4 Join Brady West, co-author of Applied Survey Data Analysis and Linear Mixed Models: A Practical Guide Using Statistical Software in a discussion about Fitting Multilevel Models to Complex Sample Survey. THURSDAY, AUGUST 5 Key Multiplicity Issues in Clinical Trials - Invited – Papers 645 Biopharmaceutical Section, Committee on Applied Statisticians, ENAR 4 Organized by Alex Dmitrienko, co-editor of Multiple Testing Problems in Pharmaceutical Statistics.
Alan E. Gelfand is the James B. Duke Professor of Statistical Science at Duke University. He is Chair of the Department of Statistical Science and Professor of Environmental Science. Author of 200 plus papers (more than 70 in the area of spatial statistics), he is internationally known for his contributions to applied statistics, Bayesian computation, and Bayesian inference. He is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Professor Gelfand is a former president of the International Society for Bayesian Analysis and in 2006 he received the Parzen Prize for a lifetime of research contribution to statistics. His primary research focus for the past 13 years has been in the area of statistical modeling for spatial and space-time data. A frequent contributor to CRC Press publications, he is co-author of the bestselling Hierarchical Modeling and Analysis for Spatial Data (new edition scheduled for 2011). He is also lead editor for the newly released Handbook of Spatial Statistics (March 2010). Through a collection of more than 60 papers Professor Gelfand has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data displays. His chief areas of application include environmental exposure, spatio-temporal ecological processes, and climatological modeling.
Now chair of Columbia University’s highly respected Department of Statistics, David Madigan uses his position to strongly advocate for the field of statistics validity as a versatile and independent science. Since receiving his Ph.D. from Trinity College in Dublin, Madigan has proven himself a prolific and ardent researcher, first at the University of Washington and then at Rutgers. Working for such companies as AT&T Inc., Soliloquy Inc., and SkillSoft, Inc, provided him with the opportunities to apply his science to modern problem solving. Decidedly Bayesian in his approach, he has over 100 publishing credits writing about a number of ways statistics intersects with other fields, including drug discovery and wireless technology. Professor Madigan is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the current Editor-inChief of Statistical Science, a peer review journal published by the Institute of Mathematical Statistics. He is also the editor of the Chapman & Hall/CRC Computer Science and Data Analysis Series. Madigan stresses the idea that statistics is no more a branch of mathematics than any other field that uses mathematical tools. He believes that the field should be producing outstanding scientists as well as outstanding mathematicians. While he recognizes that some specialization is of value, Professor Madigan has stated that specialization along applied versus theoretical lines does a disservice to the science, as that distinction reinforces the concept of a theoretical statistician developing mathematical artifacts without reference to any scientific enquiry, while the lower-browed applied statistician conducts the intellectually less rigorous job of implementing theory. For the professor, the complete statistician must concern him or herself with both, and fortunately, students at Columbia are provided with an excellent role model of such a scientist.
Bayesian Nonparametric Modeling of Longitudinal and Survival Data - Invited – Papers 553 Section on Nonparametric Statistics, Business and Economic Statistics Section, IMS, Section on Bayesian Statistical Science, Section on Health Policy Statistics, Section on Risk Analysis, WNAR 4 Wesley Johsnon, co-author of Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents Bayesian Nonparametric Longitudinal Data Analysis with Embedded Autoregressive Structure: Application to Hormone Data with Fernando Quintana.
Great Deals (continued from pg. 1)
Effective Use of Instructional Technology - Invited – Papers 647 Section on Statistical Education, Section on Statistical Computing 4 Nicholas Jon Horton, co-author of SAS and R: Data Management, Statistical Analysis, and Graphics and Using R for Data Management, Statistical Analysis, and Graphics, presents Guiding Student Project Workflow Using Reproducible Statistical Analysis Tools.
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Observed Confidence Levels, Alan M. Polansky
Analysis of Correlated Data with SAS and R, Third Edition, Mohamed M. Shoukri and Mohamed A. Chaudhary
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Published on Jul 14, 2010