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Full Description
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.
Contents
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.
