Preface
Chapter 1 Introduction
1.1 Research progress on parameter estimation
1.1.1 Advances in the estimation of regression coefficient
1.1.2 Advances in the estimation of error variance
1.2 Research progress on finite population
1.3 Plan of this book
Chapter 2 Comparisons of Biased Estimators for Regression Coefficient
2.1 Introduction
2.2 Balanced loss function and risk
. Numerical analysis
2.4 Proof of main results
Chapter 3 Comparisons of Parametric Estimation in a Misspecified Linear Model
3.1 Comparisons of estimators for regression coefficient
3.1.1 Introduction
3.1.2 Estimators and its risks
3.1.3 Comparisons of proposed estimators in theory
3.1.4 Comparisons of proposed estimators by numerical analysis
3.1.5 Simulation example
3.2 Comparisons of estimators for error variance
3.2.1 Introduction
3.2.2 Estimators and its risks
3.. Analysis of the risks
3.2.4 The bootstrap
Chapter 4 Comparisons of Preliminary Test Estimators Based on W,LR and LM Tests
4.1 Comparisons of pre-test estimators in a normal linear model
4.1 .i Introduction
4.1.2 Estimators and its risks
4.1.3 Comparison of proposed estimators
4.1.4 Simulation
4.2 Comparisons of pre-test estimators in a linear model with multivariate t distribution
4.2.1 Introduction
4.2.2 Risks of proposed estimators
4.. Comparison in theory
4.2.4 Comparison by numerical analysis
4.2.5 Comparison by bootstrap method
Chapter 5 Admissible Predictions for Finite Population Regression Coefficient
5.1 Linear admissible prediction for a general finite population
5.1.1 Introduction
5.1.2 Admissibility of a homogeneous linear predictor in the class of linear predictors
5.1.3 Admissibility of a homogeneous linear predictor in the class of all predictors
5.2 All linear admissible prediction in a finite population with respect to inequality constraints
5.2.1 Introduction
5.2.2 Admissibility of linear predictors in （数学符号）on T1
5.. Admissibility of linear predictors in （数学符号）on T1
Chapter 6 Minimax Predictions for Finite Population Regression Coefficient
6.1 Linear minimax prediction in a Gauss-Markov population
6.1.1 Introduction
6.1.2 Linear minimax predictor
6.1.3 Admissibility of LMP
6.1.4 Comparison of BLUP and LMP
6.2 Linear minimax prediction in a normal finite population
6.2.1 Introduction
6.2.2 Optimal predictor
6.. Minimax predictor
6.2.4 Comparison of BUP and MP
6.2.5 The SPP and comparison with BUP and MP
6.3 Linear minimax prediction in a finite population with ellipsoidal constraints
6.3.1 Introduction
6.3.2 Linear minimax prediction
6.3.3 Admissibility of homogeneous linear minimax prediction
6.3.4 Simulation study
6.3.5 Analysis of real data
Chapter 7 Bayesian Prediction for Finite Population ntities
7.1 Introduction
7.2 Bayes prediction of population quantities
7.3 Bayes prediction of linear quantities
7.4 Bayes prediction of quadratic quantities
7.5 Examples
References
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