Festschrift in Honor of R. Dennis Cook

1. Front Matter

   Pages i-xiii

   PDF

2. Using Mutual Information to Measure the Predictive Power of Principal Components

   • Andreas Artemiou

   Pages 1-16

3. A Robust Estimation Approach for Mean-Shift and Variance-Inflation Outliers

   • Luca Insolia, Francesca Chiaromonte, Marco Riani

   Pages 17-41

4. Estimating Sufficient Dimension Reduction Spaces by Invariant Linear Operators

   • Bing Li

   Pages 43-64

5. Testing Model Utility for Single Index Models Under High Dimension

   • Qian Lin, Zhigen Zhao, Jun S. Liu

   Pages 65-86

6. Sliced Inverse Regression for Spatial Data

   • Christoph Muehlmann, Hannu Oja, Klaus Nordhausen

   Pages 87-107

7. Model-Based Inverse Regression and Its Applications

   • Tao Wang, Lixing Zhu

   Pages 109-125

8. Sufficient Dimension Folding with Categorical Predictors

   • Yuanwen Wang, Yuan Xue, Qingcong Yuan, Xiangrong Yin

   Pages 127-165

9. Sufficient Dimension Reduction Through Independence and Conditional Mean Independence Measures

   • Yuexiao Dong

   Pages 167-180

10. Cook’s Fisher Lectureship Revisited for Semi-supervised Data Reduction

    • Jae Keun Yoo

    Pages 181-192

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.

• regression
• classification
• sufficient dimesion reduction
• graphics
• multivariate analysis
• design of experiments
• population genetics
• dimension reduction
• SDR
• Sliced Average Variance Estimation
• Minimum Discrepancy Approach
• Model-Free Variable Selection
• Sufficient Dimension Reduction Subspaces
• big data
• experimental design

• Applied Statistics, Vienna University of Technology, Vienna, Austria

  Efstathia Bura

• Department of Statistics, The Pennsylvania State University, University Park, USA

  Bing Li

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. 

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