声明
摘要
CONTENTS
List of Figures
List of Tables
1 Introduction
1.1 Background
1.2 Variable selection for regression models
1.2.1 Traditional variable selection methodologies
1.2.2 Penalized variable selection methodologies
1.3 Fundamental research aspects
1.3.1 Parametric regression models
1.3.2 High dimensional models
1.3.3 Robust regression methods
1.3.4 Quantile regression methods
1.4 Computation and selection of tuning parameters
1.4.1 The computational algorithms
1.4.2 The selection of tuning parameters
1.5 Research outlines and notations
1.5.1 Outline of the thesis
1.5.2 Notations
2 SCAD-Penalized variable selection in high dimensional linear models
2.1 Introduction
2.2 The proposed estimator
2.3 Asymptotic properties
2.4 Computation
2.4.1 Estimation procedure
2.4.2 Ultra-high dimensional case
2.4.3 Tuning parameter
2.5 Numerical studies
2.5.1 Simulation studies
2.5.2 A real application
2.6 Summary
3 Combined-Penalized variable selection in high dimensional linear models
3.1 Introduction
3.2 Combined penalized quantile regression
3.2.1 Ridge-SCAD estimation and variable selection procedure
3.2.2 Asymptotic Properties
3.3 The proofs of theorems
3.4 Computation and selection of tuning parameters
3.5 Numerical studies
3.5.1 Simulation studies
3.5.2 A real application
3.6 Summary
4 One step penalized variable selection in ultra-high dimensional linear models
4.1 Introduction
4.2 The Proposed estimator
4.3 Assumptions of model selection consistency
4.4 Proof of theorem
4.5 Simulation studies
4.6 Summary
5 Application of penalized variable selection methods in agricultural research
5.1 Introduction
5.2 Materials and methods
5.3 Results and discussions
5.3.1 Residual analysis
5.3.2 Simple correlation analysis
5.3.3 Path coefficient analysis
5.3.4 Penalized regression analysis
5.4 Summary
6 Conclusions and further Perspectives
6.1 Key aspects of proposed research
6.2 Innovation Points
6.3 New horizons for explorers
References
Published Academic Papers During PhD Period
Acknowledgement
About the Author
Dalian University of Technology Doctoral Dissertation Copyright Use Authorization