Linear Models: Least Squares and Alternatives

360 Pages·1995·10.16 MB·English

by  Calyampudi Radhakrishna Rao, Helge Toutenburg (auth.)

Springer Series in Statistics
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Springer Science+Business Media, LLC
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Fahrmeir/Tutz: Multivariate Statistical Modelling Based on Generalized Linear
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(continued after index)
c.
Radhakrishna Rao
Helge Toutenburg
Linear Models
Least Squares and Alternatives
With 33 Illustrations
Springer Science+Business Media, LLC
C. Radhakrishna Rao
The Pennsylvania State University
Department of Statistics
University Park, PA 16802
USA
Helge Toutenburg
Institut für Statistik
Universität München
80799 München
Germany
Library of Congress Cataloging-in-Publication Data
Rao, C. Radhakrishna (Calyampudi Radhakrishna), 1920-
Linear models: legist squares and alternatives / C. Radhakrishna
Rao, Helge Toutenburg.
p. cm. - (Springer series in statistics)
Includes bibliographical references (p. - ) and index.
ISBN 978-1-4899-0026-5 (hardcover : alk. paper)
1. Linear models (Statistics) I. Toutenburg, Helge. II. Title.
III. Series.
QA279.R3615 1995
5i9.5'36—dc20 95-23947
© Springer Science+Business Media New York 1995
Originally published by Springer-Verlag New York, Inc. in 1995
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987654321
ISBN 978-1-4899-0026-5 ISBN 978-1-4899-0024-1 (eBook)
DOI 10.1007/978-1-4899-0024-1
Preface
The book is based on both authors' several years of experience in teaching
linear models at various levels. It gives an up-to-date account of the theory
and applications of linear models. The book can be used as a text for
courses in statistics at the graduate level and as an accompanying text for
courses in other areas. Some of the highlights in this book are as follows.
A relatively extensive chapter on matrix theory (Appendix A) provides
the necessary tools for proving theorems discussed in the text and offers a
selection of classical and modern algebraic results that are useful in research
work in econometrics, engineering, and optimization theory. The matrix
theory of the last ten years has produced a series of fundamental results
about the definiteness of matrices, especially for the differences of matrices,
which enable superiority comparisons of two biased estimates to be made
for the first time.
We have attempted to provide a unified theory of inference from linear
models with minimal assumptions. Besides the usual least-squares theory,
alternative methods of estimation and testing based on convex loss func
tions and general estimating equations are discussed. Special emphasis is
given to sensitivity analysis and model selection.
A special chapter is devoted to the analysis of categorical data based on
logit, loglinear, and logistic regression models.
The material covered, theoretical discussion, and its practical applica
tions will be useful not only to students but also to researchers and con
sultants in statistics.
We would like to thank our colleagues Dr. G. Trenkler and Dr. V. K. Sri
vastava for their valuable advice during the preparation of the book. We
vi Preface
wish to acknowledge our appreciation of the generous help received from
Andrea Schopp, Andreas Fieger, and Christian Kastner for preparing a fair
copy. Finally, we would like to thank Dr. Martin Gilchrist of Springer-Verlag
for his cooperation in drafting and finalizing the book.
We request that readers bring to our attention any errors they may
find in the book and also give suggestions for adding new material and/or
improving the presentation of the existing material.
C. Radhakrishna Rao Helge Toutenburg
University Park, Pennsylvania Munich
USA Germany
July 1995
Contents
Preface v
1 Introduction 1
2 Linear Models 3
2.1 Regression Models in Econometrics. 3
2.2 Econometric Models ........ . 6
2.3 The Reduced Form. . . . . . . . . . 10
2.4 The Multivariate Regression Model . 12
2.5 The Classical Multivariate Linear Regression Model 15
2.6 The Generalized Linear Regression Model . . . . . . 16
3 The Linear Regression Model 19
3.1 The Linear Model ............... . 19
3.2 The Principle of Ordinary Least 8quares (OL8) 20
3.3 Geometric Properties of OL8 .. 21
3.4 Best Linear Unbiased Estimation 23
3.4.1 Basic Theorems .... . 23
3.4.2 Linear Estimators ... . 28
3.4.3 Mean Dispersion Error. . 29
3.5 Estimation (Prediction) of the Error Term e and a2 31
3.6 Classical Regression under Normal Errors 32
3.7 Testing Linear Hypotheses . . . . . . . . . 34
3.8 Analysis of Variance and Goodness of Fit 41
viii Contents
3.8.1 Bivariate Regression 41
3.8.2 Multiple Regression 46
3.8.3 A Complex Example . 50
3.8.4 Graphical Presentation 54
3.9 The Canonical Form . . . . . . 60
3.10 Methods for Dealing with Multicollinearity 61
3.10.1 Principal Components Regression. 62
3.10.2 Ridge Estimation. . . 63
3.10.3 Shrinkage Estimates . . 67
3.10.4 Partial Least Squares . 67
3.11 Projection Pursuit Regression. 71
3.12 Total Least Squares ...... 72
3.13 Minimax Estimation . . . . . . 75
3.13.1 Inequality Restrictions. 75
3.13.2 The Minimax Principle 78
3.14 Censored Regression . . . . . . 83
3.14.1 Introduction ...... 83
3.14.2 LAD Estimators and Asymptotic Normality. 84
3.14.3 Tests of Linear Hypotheses . . . . . . . . . . 85
4 The Generalized Linear Regression Model 89
4.1 Optimal Linear Estimation of {3 . . . . . . 89
4.2 The Aitken Estimator . . . . . . . . . . . 96
4.3 Misspecification of the Dispersion Matrix 98
4.4 Heteroscedasticity and Autoregression . . 101
5 Exact and Stochastic Linear Restrictions 111
5.1 Use of Prior Information. . . . . . . . . . 111
5.2 The Restricted Least-Squares Estimator . 112
5.3 Stepwise Inclusion of Exact Linear Restrictions 115
5.4 Biased Linear Restrictions and MDE Comparison with the
OLSE .............................. 120
5.5 MDE Matrix Comparisons of Two Biased Estimators. . . . 123
5.6 MDE Matrix Comparison of Two Linear Biased Estimators 129
5.7 MDE Comparison of Two (Biased) Restricted Estimators 130
5.7.1 Special Case: Stepwise Biased Restrictions. 134
5.8 Stochastic Linear Restrictions . . . . . . . . . . . . 138
5.8.1 Mixed Estimator . . . . . . . . . . . . . . . 138
5.8.2 Assumptions about the Dispersion Matrix . 140
5.8.3 Biased Stochastic Restrictions. 143
5.9 Weakened Linear Restrictions. . . . . . . . . . . . 147
5.9.1 Weakly (R, r)-Unbiasedness . . . . . . . . . 147
5.9.2 Optimal Weakly (R, r)-Unbiased Estimators. 148
5.9.3 Feasible Estimators-Optimal Substitution of {3 in
,61({3,A) ......................... 152
Contents ix
5.9.4 RLSE Instead of the Mixed Estimator . . . . . . .. 153
6 Prediction Problems in the Generalized Regression Mode1155
6.1 Introduction......... 155
6.2 Some Simple Linear Models . . . . 155
6.3 The Prediction Model . . . . . . . 158
6.4 Optimal Heterogeneous Prediction 159
6.5 Optimal Homogeneous Prediction. 161
6.6 MDE Matrix Comparisons between Optimal and Classical
Predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
6.6.1 Comparison of Classical and Optimal Prediction with
Respect to the y*-Superiority . . . . . . . . . . . . . 167
6.6.2 Comparison of Classical and Optimal Predictors with
Respect to the X*,B-Superiority . 170
6.7 Prediction Regions . . . . . . . . . . . . . . . . . . . . . . . 172
7 Sensitivity Analysis 177
7.1 Introduction ... 177
7.2 Prediction Matrix. . . . . . . . . . . . . . . . . . . . . . . . 177
7.3 The Effect of a Single Observation on the Estimation of Pa-
rameters . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 183
7.3.1 Measures Based on Residuals . . . . . . . . . . . .. 184
7.3.2 Algebraic Consequences of Omitting an Observation 185
7.3.3 Detection of Outliers . . . . . . . . . . . . . . 186
7.4 Diagnostic Plots for Testing the Model Assumptions 191
7.5 Measures Based on the Confidence Ellipsoid. 192
7.6 Partial Regression Plots . . . . . . . . . . . . 199
8 Analysis of Incomplete Data Sets 203
8.1 Statistical Analysis with Missing Data . . . . . . . 203
8.2 Missing Data in the Response . . . . . . . . . . . . 209
8.2.1 Least-Squares Analysis for Complete Data. 209
8.2.2 Least-Squares Analysis for Filled-up Data . 210
8.2.3 Analysis of Covariance-Bartlett's Method 211
8.3 Missing Values in the X-Matrix. . . . . . . . . . . 212
8.3.1 Missing Values and Loss in Efficiency ... 214
8.3.2 Standard Methods for Incomplete X-Matrices. 216
8.4 Maximum Likelihood Estimates of Missing Values 219
8.5 Weighted Mixed Regression . . 221
8.5.1 Minimizing the MDEP . 223
8.5.2 The Two-Stage WMRE 226
9 Robust Regression 229
9.1 Introduction ... 229
9.2 Least Absolute Deviation Estimators-Univariate Case. 230