刚刚查了一下才发现这本书原来已经有了，不过我没办法，要期中考试了急需论坛上的一些资料，还是发了上来。人家是20金币，我只要3金币。希望对这点金币无所谓的好心人帮帮忙，留学在外不想输给人家啊。而且这本书也挺不错的。在此先谢过了！！！管理员也高抬贵手啊。


书名：Linear Models: Least Squares and Alternatives, Second Edition
作者：C. Radhakrishna Rao, Helge Toutenburg
出版社：Springer
大小：1.73M 493pages
参考目录：
1 Introduction 1
2 Linear Models 5
2.1 RegressionModels in Econometrics . . . . . . . . . . . . 5
2.2 EconometricModels . . . . . . . . . . . . . . . . . . . . . 8
2.3 The Reduced Form . . . . . . . . . . . . . . . . . . . . . 12
2.4 TheMultivariate RegressionModel . . . . . . . . . . . . 14
2.5 The Classical Multivariate Linear Regression Model . . . 17
2.6 The Generalized Linear RegressionModel . . . . . . . . . 18
2.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 The Linear Regression Model 23
3.1 The LinearModel . . . . . . . . . . . . . . . . . . . . . . 23
3.2 The Principle of Ordinary Least Squares (OLS) . . . . . 24
3.3 Geometric Properties of OLS . . . . . . . . . . . . . . . . 25
3.4 Best Linear Unbiased Estimation . . . . . . . . . . . . . 27
3.4.1 Basic Theorems . . . . . . . . . . . . . . . . . . . 27
3.4.2 Linear Estimators . . . . . . . . . . . . . . . . . 32
3.4.3 Mean Dispersion Error . . . . . . . . . . . . . . . 33
3.5 Estimation (Prediction) of the Error Term
and σ2 . . . 34
x Contents
3.6 Classical Regression under Normal Errors . . . . . . . . . 35
3.6.1 TheMaximum-Likelihood (ML) Principle . . . . 36
3.6.2 ML Estimation in Classical Normal Regression . 36
3.7 Testing Linear Hypotheses . . . . . . . . . . . . . . . . . 37
3.8 Analysis of Variance and Goodness of Fit . . . . . . . . . 44
3.8.1 Bivariate Regression . . . . . . . . . . . . . . . . 44
3.8.2 Multiple Regression . . . . . . . . . . . . . . . . 49
3.8.3 A Complex Example . . . . . . . . . . . . . . . . 53
3.8.4 Graphical Presentation . . . . . . . . . . . . . . 56
3.9 The Canonical Form. . . . . . . . . . . . . . . . . . . . . 57
3.10 Methods for Dealing with Multicollinearity . . . . . . . . 59
3.10.1 Principal Components Regression . . . . . . . . . 59
3.10.2 Ridge Estimation . . . . . . . . . . . . . . . . . . 60
3.10.3 Shrinkage Estimates . . . . . . . . . . . . . . . . 64
3.10.4 Partial Least Squares . . . . . . . . . . . . . . . 65
3.11 Projection Pursuit Regression . . . . . . . . . . . . . . . 68
3.12 Total Least Squares . . . . . . . . . . . . . . . . . . . . . 70
3.13 Minimax Estimation. . . . . . . . . . . . . . . . . . . . . 72
3.13.1 Inequality Restrictions . . . . . . . . . . . . . . . 72
3.13.2 TheMinimax Principle . . . . . . . . . . . . . . 75
3.14 Censored Regression . . . . . . . . . . . . . . . . . . . . . 80
3.14.1 Overview . . . . . . . . . . . . . . . . . . . . . . 80
3.14.2 LAD Estimators and Asymptotic Normality . . . 81
3.14.3 Tests of Linear Hypotheses . . . . . . . . . . . . 82
3.15 Simultaneous Confidence Intervals . . . . . . . . . . . . . 84
3.16 Confidence Interval for the Ratio of Two Linear
Parametric Functions . . . . . . . . . . . . . . . . . . . . 85
3.17 Neural Networks and Nonparametric Regression . . . . . 86
3.18 Logistic Regression and Neural Networks . . . . . . . . . 87
3.19 Restricted Regression . . . . . . . . . . . . . . . . . . . . 88
3.19.1 Problemof Selection . . . . . . . . . . . . . . . . 88
3.19.2 Theory of Restricted Regression . . . . . . . . . 88
3.19.3 Efficiency of Selection . . . . . . . . . . . . . . . 91
3.19.4 Explicit Solution in Special Cases . . . . . . . . . 91
3.20 Complements . . . . . . . . . . . . . . . . . . . . . . . . 93
3.20.1 LinearModels withoutMoments: Exercise . . . . 93
3.20.2 Nonlinear Improvement of OLSE for
Nonnormal Disturbances . . . . . . . . . . . . . . 93
3.20.3 A Characterization of the Least Squares
Estimator . . . . . . . . . . . . . . . . . . . . . . 94
3.20.4 A Characterization of the Least Squares
Estimator: A Lemma . . . . . . . . . . . . . . . . 94
3.21 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4 The Generalized Linear Regression Model 97
Contents xi
4.1 Optimal Linear Estimation of β . . . . . . . . . . . . . . 97
4.1.1 R1-Optimal Estimators . . . . . . . . . . . . . . 98
4.1.2 R2-Optimal Estimators . . . . . . . . . . . . . . 102
4.1.3 R3-Optimal Estimators . . . . . . . . . . . . . . 103
4.2 The Aitken Estimator . . . . . . . . . . . . . . . . . . . . 104
4.3 Misspecification of the DispersionMatrix . . . . . . . . . 106
4.4 Heteroscedasticity and Autoregression . . . . . . . . . . . 109
4.5 Mixed Effects Model: A Unified Theory of Linear
Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.5.1 Mixed EffectsModel . . . . . . . . . . . . . . . . 117
4.5.2 A Basic Lemma . . . . . . . . . . . . . . . . . . 118
4.5.3 Estimation of Xβ (the Fixed Effect) . . . . . . . 119
4.5.4 Prediction of Uξ (the RandomEffect) . . . . . . 120
4.5.5 Estimation of
. . . . . . . . . . . . . . . . . . . 120
4.6 Regression-Like Equations in Econometrics . . . . . . . . 121
4.6.1 Stochastic Regression . . . . . . . . . . . . . . . 121
4.6.2 Instrumental Variable Estimator . . . . . . . . . 122
4.6.3 Seemingly Unrelated Regressions . . . . . . . . . 123
4.7 Simultaneous Parameter Estimation by Empirical
Bayes Solutions . . . . . . . . . . . . . . . . . . . . . . . 124
4.7.1 Overview . . . . . . . . . . . . . . . . . . . . . . 124
4.7.2 Estimation of Parameters from Different
LinearModels . . . . . . . . . . . . . . . . . . . 126
4.8 Supplements . . . . . . . . . . . . . . . . . . . . . . . . . 130
4.9 Gauss-Markov, Aitken and Rao Least Squares Estimators 130
4.9.1 Gauss-Markov Least Squares . . . . . . . . . . . 131
4.9.2 Aitken Least Squares . . . . . . . . . . . . . . . . 132
4.9.3 Rao Least Squares . . . . . . . . . . . . . . . . . 132
4.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
5 Exact and Stochastic Linear Restrictions 137
5.1 Use of Prior Information . . . . . . . . . . . . . . . . . . 137
5.2 The Restricted Least-Squares Estimator . . . . . . . . . 138
5.3 Stepwise Inclusion of Exact Linear Restrictions . . . . . . 141
5.4 Biased Linear Restrictions and MDE Comparison with
the OLSE . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.5 MDE Matrix Comparisons of Two Biased Estimators . . 149
5.6 MDE Matrix Comparison of Two Linear Biased Estimators 154
5.7 MDE Comparison of Two (Biased) Restricted Estimators 156
5.8 Stochastic Linear Restrictions . . . . . . . . . . . . . . . 163
5.8.1 Mixed Estimator . . . . . . . . . . . . . . . . . . 163
5.8.2 Assumptions about the DispersionMatrix . . . . 165
5.8.3 Biased Stochastic Restrictions . . . . . . . . . . . 168
5.9 Weakened Linear Restrictions . . . . . . . . . . . . . . . 172
5.9.1 Weakly (R, r)-Unbiasedness . . . . . . . . . . . . 172
xii Contents
5.9.2 Optimal Weakly (R, r)-Unbiased Estimators . . . 173
5.9.3 Feasible Estimators—Optimal Substitution of β in
ˆ β1(β,A) . . . . . . . . . . . . . . . . . . . . . . . 176
5.9.4 RLSE instead of the Mixed Estimator . . . . . . 178
5.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . 179