Theory of Ridge Regression Estimation with Applications

Mohammad Arashi, A. K. Md. Ehsanes Saleh, B. M. Golam Kibria

ISBN 10: 1118644611 / ISBN 13: 9781118644614

Published by John Wiley and Sons Inc, US, 2019

About this Item

A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications 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. 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: Offers theoretical coverage and computer-intensive applications of the procedures presentedContains solutions and alternate methods for prediction accuracy and selecting model proceduresPresents the first book to focus on ridge regression and unifies past research with current methodologyUses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.

Seller Inventory # LU-9781118644614

Bibliographic Details

• Title: Theory of Ridge Regression Estimation with Applications
• Publisher: John Wiley and Sons Inc, US
• Publication Date: 2019
• Language: English
• ISBN 10: 1118644611
• ISBN 13: 9781118644614
• Binding: Hardback
• Condition: New
• Weight: 742 grams

About this title

Synopsis

A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications

Theory of Ridge Regression Estimation with Applications 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.

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:

• Offers theoretical coverage and computer-intensive applications of the procedures presented
• Contains solutions and alternate methods for prediction accuracy and selecting model procedures
• Presents the first book to focus on ridge regression and unifies past research with current methodology
• Uses R throughout the text and includes a companion website containing convenient data sets

Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.

About the Author

A. K. Md. EHSANES SALEH, PhD, is a Professor Emeritus and Distinguished Research Professor in the school of Mathematics and Statistics, Carleton University, Ottawa, Canada.

MOHAMMAD ARASHI, PhD, is an Associate Professor at Shahrood University of Technology, Iran and Extraordinary Professor and C2 rated researcher at University of Pretoria, Pretoria, South Africa.

B. M. GOLAM KIBRIA, PhD, is a Professor in the Department of Mathematics and Statistics at Florida International University, Miami, FL.