Synopses & Reviews
While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
Review
From the reviews: MATHEMATICAL REVIEWS "Despite its high level, the book is extremely readable and gives new insight into the problem of estimation in the linear regression model."
Synopsis
Linear regression is an important area of statistics, theoretical or applied. There have been a large number of estimation methods proposed and developed for linear regression. Each has its own competitive edge but none is good for all purposes. This manuscript focuses on construction of an adaptive combination of two estimation methods. The purpose of such adaptive methods is to help users make an objective choice and to combine desirable properties of two estimators.
Synopsis
This book presents some recent developments in the theory of robust estimation of linear regression models. It will serve as a reference book in post graduate courses.
Table of Contents
Prologue.- Regression Methods.- Adaptive LAD + LS Regression.- Adaptive LAD +_ TLS Regression.- Adaptive LAD + M-Regression.- Adaptive LS & TLS Regression.- Adaptive Choice of Trimming Proportions.- Adaptive Combination of Tests.- Computational Aspects.