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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.
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.
€111.99
About the author
A. K. Md. EHSANES SALEH, Ph D, is a Professor Emeritus and Distinguished Research Professor in the school of Mathematics and Statistics, Carleton University, Ottawa, Canada.MOHAMMAD ARASHI, Ph D, 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, Ph D, is a Professor in the Department of Mathematics and Statistics at Florida International University, Miami, FL.
Language English ● Format EPUB ● Pages 384 ● ISBN 9781118644508 ● File size 20.9 MB ● Publisher John Wiley & Sons ● Published 2019 ● Edition 1 ● Downloadable 24 months ● Currency EUR ● ID 6817237 ● Copy protection Adobe DRM
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