Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data

Dr. S. Ejaz Ahmed is Professor of Statistics and Dean of the Faculty of Math and Science at Brock University, Canada. Previously, he was Professor and Head of the Mathematics and Statistics Department at the University of Windsor, Canada and University of Regina, Canada as well as Assistant Professor at the University of the Western Ontario, Canada. He holds adjunct professorship positions at many Canadian and International universities. He has supervised more than 20 Ph.D. Students, and organized several international workshops and conferences around the globe. He is a Fellow of the American Statistical Association and held prestigious ASEAN Chair Professorship position. His areas of expertise include big data analysis, statistical learning, and shrinkage estimation strategy. Having authored several books, he edited and co-edited several volumes and special issues of scientific journals. He is Technometrics Review Editor for past ten years. Further, he is Editor and associate editor of many statistical journals. Overall, he published more than 200 articles in scientific journals and reviewed more than 100 books. Having been among the Board of Directors of the Statistical Society of Canada, he was also Chairman of its Education Committee. Moreover, he was Vice President of Communications for The International Society for Business and Industrial Statistics (ISBIS) as well as a member of the "Discovery Grants Evaluation Group" and the "Grant Selection Committee" of the Natural Sciences and Engineering Research Council of Canada.Feryaal Ahmed is a Management Science PhD candidate at Ivey Business School, Western University. Her research interests are in data analytics, machine learning, and revenue management, specifically in modeling pricing strategies for service industries that offer ancillary items.Bahadır Yüzbaşı is an Associate Professor at Inonu University. He received his Doctorate from Inonu University in 2014 under the co-supervision of Professor Ahmed. He has been working on big data and statistical machine learning techniques with theory and applications, as well as professionally coding his studies in R and publishing them on CRAN. He has written a number of articles and chapters for books that have been published by well-known publishers.

• 1. Introduction 2. Introduction to Machine Learning 3. Post Shrinkage Strategies in Sparse Regression Models 4. Shrinkage Strategies in High-dimensional Regression Model 5. Shrinkage Estimation Strategies in Partially Linear Models 6. Shrinkage Strategies : Generalized Linear Models 7. Post Shrinkage Strategy in Sparse Linear Mixed Models 8. Shrinkage Estimation in Sparse Nonlinear Regression Models 9. Shrinkage Strategies in Sparse Robust Regression Models 10. Liu-type Shrinkage Estimations in Linear Sparse Models

"Recently, focus has been on estimation techniques after a model selection via shrinkage approaches. The literature in this field has witnessed a huge growth which created a dire need to collect these developments in a single source. This book is a timely answer to such need.The book is strongly recommended for statistical researchers as well as practitioners intending to advance or apply penalty and shrinkage estimation methodologies."Abdulkadir Hussein, University of Windsor, Canada, Technometrics, November 2023."This book in some ways may be little ahead of its time but provides a potential approach to modelling, via regression, in a high dimensional setting while utilizing the idea of shrinkage. The basic premise is that no piece of information should be thrown away, yet, information can be weighted by its importance. The authors have considered the high dimensional setting with linear, generalized and nonlinear models."Ravindra Khattree, Oakland University, USA, International Statistical Review, June 2024."The book ‘Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data’ fills a critical gap, examining shrinkage strategy against penalty, full model, and submodel estimators. It offers foundational insights into model selection and estimation challenges, uniting theory with practicality. A valuable resource for statisticians and practitioners navigating complex big data scenarios."Shuangzhe Liu, University of Canberra, Australia, Journal of Applied Statistics, November 2023.