Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data
1st Edition
Published on 25. May 2023
408 pages
978-1-000-87668-0 (ISBN)
Description
This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in data science. It combines statistical learning and machine learning techniques in a unique and optimal way. It is well-known that machine learning methods are subject to many issues relating to bias, and consequently the mean squared error and prediction error may explode. For this reason, we suggest shrinkage strategies to control the bias by combining a submodel selected by a penalized method with a model with many features. Further, the suggested shrinkage methodology can be successfully implemented for high dimensional data analysis. Many researchers in statistics and medical sciences work with big data. They need to analyse this data through statistical modelling. Estimating the model parameters accurately is an important part of the data analysis. This book may be a repository for developing improve estimation strategies for statisticians. This book will help researchers and practitioners for their teaching and advanced research, and is an excellent textbook for advanced undergraduate and graduate courses involving shrinkage, statistical, and machine learning.
The book succinctly reveals the bias inherited in machine learning method and successfully provides tools, tricks and tips to deal with the bias issue.
Expertly sheds light on the fundamental reasoning for model selection and post estimation using shrinkage and related strategies.
This presentation is fundamental, because shrinkage and other methods appropriate for model selection and estimation problems and there is a growing interest in this area to fill the gap between competitive strategies.
Application of these strategies to real life data set from many walks of life.
Analytical results are fully corroborated by numerical work and numerous worked examples are included in each chapter with numerous graphs for data visualization.
The presentation and style of the book clearly makes it accessible to a broad audience. It offers rich, concise expositions of each strategy and clearly describes how to use each estimation strategy for the problem at hand.
This book emphasizes that statistics/statisticians can play a dominant role in solving Big Data problems, and will put them on the precipice of scientific discovery.
The book contributes novel methodologies for HDDA and will open a door for continued research in this hot area.
The practical impact of the proposed work stems from wide applications. The developed computational packages will aid in analyzing a broad range of applications in many walks of life.
The book succinctly reveals the bias inherited in machine learning method and successfully provides tools, tricks and tips to deal with the bias issue.
Expertly sheds light on the fundamental reasoning for model selection and post estimation using shrinkage and related strategies.
This presentation is fundamental, because shrinkage and other methods appropriate for model selection and estimation problems and there is a growing interest in this area to fill the gap between competitive strategies.
Application of these strategies to real life data set from many walks of life.
Analytical results are fully corroborated by numerical work and numerous worked examples are included in each chapter with numerous graphs for data visualization.
The presentation and style of the book clearly makes it accessible to a broad audience. It offers rich, concise expositions of each strategy and clearly describes how to use each estimation strategy for the problem at hand.
This book emphasizes that statistics/statisticians can play a dominant role in solving Big Data problems, and will put them on the precipice of scientific discovery.
The book contributes novel methodologies for HDDA and will open a door for continued research in this hot area.
The practical impact of the proposed work stems from wide applications. The developed computational packages will aid in analyzing a broad range of applications in many walks of life.
Reviews / Votes
"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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Product notice
Reflowable
Illustrations
127 Tables, black and white; 60 Line drawings, color; 5 Line drawings, black and white; 2 Halftones, color; 62 Illustrations, color; 5 Illustrations, black and white
ISBN-13
978-1-000-87668-0 (9781000876680)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Syed Ejaz Ahmed | Feryaal Ahmed | Bahadir Yuezbasi
Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data
Book
05/2023
1st Edition
Chapman & Hall/CRC
€184.90
Shipment within 15-20 days
Persons
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.
Bahadir Yuezbasi 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.
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.
Bahadir Yuezbasi 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.
Content
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
