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.
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"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.
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.