Adaptive Regression
Published on 24. September 2012
Book
Paperback/Softback
XII, 177 pages
978-1-4612-6464-4 (ISBN)
Description
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.
Reviews / Votes
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."
More details
Edition
Softcover reprint of the original 1st ed. 2000
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XII, 177 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 11 mm
Weight
300 gr
ISBN-13
978-1-4612-6464-4 (9781461264644)
DOI
10.1007/978-1-4419-8766-2
Schweitzer Classification
Other editions
Additional editions
Yadolah Dodge | Jana Jureckova
Adaptive Regression
Book
04/2000
Springer
€85.59
Shipment within 5-7 days
Content
1 Prologue.- 1.1 Introduction.- 1.2 Adaptive Combination of Estimators.- 1.3 Notes.- 2 Regression Methods.- 2.1 Introduction.- 2.2 LS Regression.- 2.3 Ridge Regression.- 2.4 LAD Regression.- 2.5 M-Regression.- 2.6 L-Regression.- 2.7 Other Regression Estimators.- 2.8 Estimators of Scale Parameter.- 2.9 Notes.- 3 Adaptive LAD + LS Regression.- 3.1 Introduction.- 3.2 Convex Combination of LAD and LS Regressions.- 3.3 Adaptive Combination of LAD and LS Regressions.- 3.4 Illustrative Examples.- 3.5 Notes.- 4 Adaptive LAD + TLS Regression.- 4.1 Introduction.- 4.2 Adaptive Combination of LAD and Trimmed LS.- 4.3 An Example of Multiple Regression.- 4.4 Notes.- 5 Adaptive LAD + M-Regression.- 5.1 Introduction.- 5.2 Combination of LAD and M-Estimators.- 5.3 Adaptive Combination of LAD and M-Estimators.- 5.4 An Example of Multiple Regression.- 5.5 Notes.- 6 Adaptive LS + TLS Regression.- 6.1 Introduction.- 6.2 Adaptive Combination of Mean and Trimmed Mean.- 6.3 Adaptive Combination of LS and TLS Regressions.- 6.4 Example of Multiple Regression.- 6.5 Notes.- 7 Adaptive Choice of Trimming.- 7.1 Introduction.- 7.2 Fully Adaptive Trimmed Mean and TLS.- 7.3 Adaptive Choice for fhe Trimmed Mean.- 7.4 Adaptive Choice in Linear Model Based on Ranks.- 7.5 Adaptive Choice in Linear Model Based on Regression Rank Scores.- 7.6 Notes.- 8 Adaptive Combination of Tests.- 8.1 Introduction.- 8.2 Types of Tests.- 8.3 Adaptive Combination of F-Test and Median-Type Test.- 8.4 Adaptive Combination of M-Test and Median-Type Test.- 8.5 Notes.- 9 Computational Aspects.- 9.1 Introduction.- 9.2 Computing the Adaptive Combination of LS and LAD.- 9.3 Program ADAPTIVE.- 10 Some Asymptotic Results.- 10.1 Asymptotic Properties of Studentized M-Estimators.- 10.2 Uniform Asymptotic Linearity of M-Statistics.- 10.3 Estimators of Scale Parameter.- 10.4 Optimal Choice of ?n.- 11 Epilogue.- References.- Author Index.