Theory of Ridge Regression Estimation with Applications
- English
- PDF
- Available on iOS & Android
Theory of Ridge Regression Estimation with Applications
About this book
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.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Title Page
- Copyright
- Contents
- List of Figures
- List of Tables
- Preface
- Abbreviations and Acronyms
- List of Symbols
- Chapter 1 Introduction to Ridge Regression
- Chapter 2 Location and Simple Linear Models
- Chapter 3 ANOVA Model
- Chapter 4 Seemingly Unrelated Simple Linear Models
- Chapter 5 Multiple Linear Regression Models
- Chapter 6 Ridge Regression in Theory and Applications
- Chapter 7 Partially Linear Regression Models
- Chapter 8 Logistic Regression Model
- Chapter 9 Regression Models with Autoregressive Errors
- Chapter 10 Rank-Based Shrinkage Estimation
- Chapter 11 High-Dimensional Ridge Regression
- Chapter 12 Applications: Neural Networks and Big Data
- References
- Index
- EULA