Rank-Based Methods for Shrinkage and Selection

The book aims to accumulate the different theory and methods for selection and shrinkage estimation based on rank order. These theories are intended to be systematically organized to serve as a guide for researchers in this field. 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 analysis and machine learning. Written by noted experts in this field, this book contains a through introduction to robust rank-based penalty and shrinkage estimations and explores role of ridge, LASSO and Elastic Net play in the computer intuitive environment for big data analysis. Designed to be accessible, this book presets detailed coverage of the basic terminology related to the various models such as the location and simple linear models, and rank-theory based ridge, LASSO and Elastic Net and Sein-type estimators. This book provides a unified presentation of various methods in one book and has potential use in machine learning.