Theory of Ridge Regression Estimation with Applications

<p><b>A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications</b></p> <p><i>Theory of Ridge Regression Estimation with Applications</i> offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis.</p> <p>Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators.
The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource:</p> <ul> <li>Offers theoretical coverage and computer-intensive applications of the procedures presented</li> <li>Contains solutions and alternate methods for prediction accuracy and selecting model procedures</li> <li>Presents the first book to focus on ridge regression and unifies past research with current methodology</li> <li>Uses R throughout the text and includes a companion website containing convenient data sets</li> </ul> <p>Written for graduate students, practitioners, and researchers in various fields of science, <i>Theory of Ridge Regression Estimation with Applications</i> is an authoritative guide to the theory and methodology of statistical estimation.</p>