Festschrift in Honor of R. Dennis Cook

In honor of professor and renowned statistician R. Dennis Cook, this festschrift explores his influential contributions to an array of statistical disciplines ranging from experimental design and population genetics, to statistical diagnostics and all areas of regression-related inference and analysis. Since the early 1990s, Prof. Cook has led the development of dimension reduction methodology in three distinct but related regression contexts: envelopes, sufficient dimension reduction (SDR), and regression graphics. In particular, he has made fundamental and pioneering contributions to SDR, inventing or co-inventing many popular dimension reduction methods, such as sliced average variance estimation, the minimum discrepancy approach, model-free variable selection, and sufficient dimension reduction subspaces.
A prolific researcher and mentor, Prof. Cook is known for his ability to identify research problems in statistics that are both challenging and important, as well as his deep appreciation for the applied side of statistics. This collection of Prof. Cook's collaborators, colleagues, friends, and former students reflects the broad array of his contributions to the research and instructional arenas of statistics.

Traces the inspiring career path and work of outstanding statistician R. Dennis Cook. Presents contemporary concepts and trends in sufficient dimension reduction (SDR), envelope models, and data visualization. Includes a comprehensive list of important papers in the featured topics.

Autorentext

Dr. Efstathia Bura is professor and chair of applied statistics at the Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, where she heads the Applied Statistics Research Unit (ASTAT). Her work has been published in numerous journals, including Journal of the American Statistical Association, Journal of Multivariate Analysis, Statistics in Medicine, and Biometrics. Her research focuses on dimension reduction in regression and classification, high-dimensional statistics, multivariate analysis, and applications in biostatistics, econometrics and legal statistics. Dr. Bing Li is Verne M. Willaman Professor of statistics at Pennsylvania State University. His work has been published in many journals, including Journal of the American Statistical Association, The Annals of Statistics, Biometrika, and the Journal of the Royal Statistical Society, Series B. His research interests include dimension reduction, machine learning, statistical graphical models, functional data analysis, and estimating equations. He has served as an Associate Editor for the Annals of Statistics and Journal of the American Statistical Society.

Inhalt
Sufficient dimension reduction through independence and conditional mean independence measures - Yuexiao Dong.- Model-based inverse regression and its applications - Tao Wang and Lixing Zhu.- Cook's Fisher Lectureship revisited for semi-supervised data reduction - Jae Keun Yoo.- Global testing under sparse alternatives for single index models - Qian Lin, Zhigen Zhao, and Jin Liu.- Supervised dimension reduction for spatian data - Christoph Muehlmann, Hanna Oja, and Klaus Nordhausen.- Sufficient dimension folding with categorical predictors - Yuanwen Wang, Yuan Xue, Qingcong Yuan, and Xiangrong Yin.