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Theory of ridge regression estimation with applications/ A. K. Md. Ehsanes Saleh, Mohammad Arashi and B.M. Golam Kibria

By: Contributor(s): Series: Wiley Series in Probability and StatisticsPublication details: New Jersey: Wiley, 2019Description: xxxiv, 342 pages, 23.5 cmISBN:
  • 9781118644614
Subject(s): DDC classification:
  • 23 000SA.06 Eh33
Contents:
1. Introduction to Ridge regression -- 2. Location and simple linear models -- 3. ANOVA model -- 4. Seemingly unrelated simple linear models -- 5. Multiple linear regression models -- 6. Ridge regression in theory and applications -- 7. Partially linear regression models -- 8. Logistic regression model -- 9. Regression model -- 9. Regression models with autoregressive errors -- 10. Rank-based shrinkage estimation -- 11. High dimensional Ridge regression -- 12. Applications: neural networks and big data
Summary: This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. It 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.
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Includes bibliographical references and index

1. Introduction to Ridge regression -- 2. Location and simple linear models -- 3. ANOVA model -- 4. Seemingly unrelated simple linear models -- 5. Multiple linear regression models -- 6. Ridge regression in theory and applications -- 7. Partially linear regression models -- 8. Logistic regression model -- 9. Regression model -- 9. Regression models with autoregressive errors -- 10. Rank-based shrinkage estimation -- 11. High dimensional Ridge regression -- 12. Applications: neural networks and big data

This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. It 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.

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