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.
