描述
开 本: 16开纸 张: 胶版纸包 装: 平装-胶订是否套装: 否国际标准书号ISBN: 9787509661024
1．1 Introduction
1．2 Robust Regression Methods
1．2．1 M-estimates
1．2．2 LMS estimates
1．2．3 LTS estimates
1．2．4 S-estimates
1．2．5 Generalized S-estimates （GS-estimates）
1．2．6 MM-estimates
1．2．7 Mallows GM-estimates
1．2．8 Schweppe GM-estimates
1．2．9 S1S GM-estimates
1．2．10 R-estimates
1．2．11 REWLSE
1．2．12 Robust regression based on regularization of case-specific parameters
1．3 Examples
1．4 Discussion
Chapter 2 A Selective Overview and Comparison of Robust Mixture Regression Estimators
2．1 Introduction
2．2 Robust mixture regression methods
2．2．1 Robust mixture regresion using the t-distribution
2．2．2 Robust mixture regression modeling using Pearson type VM distribution
2．2．3 Robust mixture regression model fitting by Laplace distribution
2．2．4 Robust mixture regression modeling based on Scale mixtures of skew-normal distributions
2．2．5 Robust mixture regression with random covariates via trimming and constraints
2．2．6 Robust clustering in regression analysis via the contaminated gaussian cluster weighted model
2．2．7 Trimmed likelihood estimator
2．2．8 Least trimmed squares estimator
2．2．9 Robust estimator based on a modified EM algorithm with bisquare loss
2．2．10 Robust EM-type algorithm for log-concave mixtures of regression models
2．3 Simulation studies
2．4 Discussion
Chapter 3 Outlier Detection and Robust Mixture Modeling Using Nonconvex Penalized Likelihood
3．1 Introduction
3．2 Robust Mixture Model via Mean-Shift Penalization
3．2．1 RMM for Equal Component Variances
3．2．2 RMM for Unequal Component Variances
3．2．3 Tuning Parameter Selection
3．3 Simulation
3．3．1 Methods and Evaluation Measures
3．3．2 Results
3．4 Real Data Application
3．5 Discussion
Chapter 4 Outlier Detection and Robust Mixture Regression Using Nonconvex Penalized Likelihood
4．1 Introduction
4．2 Robust Mixture Regression via Mean-shift Penalization
4．3 Simulation
4．3．1 Simulation Setups
4．3．2 Methods and Evaluation Measures
4．3．3 Results
4．4 Tone Perception Data Analysis
4．5 Discussion
Appendix
References
评论
还没有评论。