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Chapter 1 Robust Linear Regression£º A Review and Comparison
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