Advances in Shrinkage and Penalized Estimation Strategies
Honoring the Contributions of Professor A. K. Md. Ehsanes Saleh
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Beschreibung
This book is a tribute to Professor A. K. Md. Ehsanes Saleh, a distinguished figure in the field of statistics known for his pioneering work, including the development of the "Preliminary Test Approach to Shrinkage Estimation". Although Professor Saleh passed away in September 2023, his legacy will live on through this special volume that explores penalized approaches for statistical analysis and recent developments in shrinkage methods. Covering regression modeling, robust estimation, machine learning, and high-dimensional data analytics, this volume bridges theoretical advancements with practical applications of these methodologies.
In addition to introducing novel research and viewpoints, the book seeks to encourage dialogue among experienced practitioners in the field. This resource is specifically designed for researchers, statisticians, or data science professionals seeking ways to improve their comprehension and application of these methods.
This book is a tribute to Professor A. K. Md. Ehsanes Saleh, a distinguished figure in the field of statistics known for his pioneering work, including the development of the "Preliminary Test Approach to Shrinkage Estimation". Although Professor Saleh passed away in September 2023, his legacy will live on through this special volume that explores penalized approaches for statistical analysis and recent developments in shrinkage methods. Covering regression modeling, robust estimation, machine learning, and high-dimensional data analytics, this volume bridges theoretical advancements with practical applications of these methodologies.
In addition to introducing novel research and viewpoints, the book seeks to encourage dialogue among experienced practitioners in the field. This resource is specifically designed for researchers, statisticians, or data science professionals seeking ways to improve their comprehension and application of these methods.
Examines statistical shrinkage estimation methods in a wide range of modeling situations Combines research with practical applications in regression, machine learning, and data analytics Presents in-depth views at statistical methods in the area of shrinkage estimation
Autor*in
Mohammad Arashi
Themen in »Advances in Shrinkage and Penalized Estimation Strategies«
Shrinkage Estimation Unrestricted Estimation Restricted Estimation Preliminary Test Estimation Stein-type Estimation Positive-rule Stein-type Estimation James-Stein Estimation Tackling Multicollinearity Liu Estimation Ridge Regression LASSO Estimation Penalized Estimation Biased Estimation Balanced Loss Function Robust Estimation