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

This book presents post-estimation and predictions strategies for the host of useful statistical models with applications in data science. Combining statistical learning and machine learning techniques in a unique and optimal way, it will help professionals in their teaching and advanced research.

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.