稳健混合模型/江西财经大学东亿学术论丛

作者简介

余纯，统计学博士，现任江西财经大学统计学院副教授。研究方向为稳健线性回归、稳健混合模型、变量与模型选择以及精算科学等。主要讲授“金融数学”“精算概率”“概率论”“线性模型方法”以及“数理统计前沿问题研究”等大学本科和研究生课程。

内容简介

书名:稳健混合模型

定价：68.00元

作者:余纯 著

出版社：经济管理出版社

出版日期：2020-03-01

ISBN：9787509661024

字数：

页码：155

版次：

装帧：平装

开本：16开

商品重量：

书籍目录

Chapter 1 Robust Linear Regression： A Review and Compariso
1．1 Introductio
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 Discussio
Chapter 2 A Selective Overview and Comparison of Robust Mixture Regression Estimators
2．1 Introductio
2．2 Robust mixture regression methods
2．2．1 Robust mixture regresion using the t-distributio
2．2．2 Robust mixture regression modeling using Pearson type VM distributio
2．2．3 Robust mixture regression model fitting by Laplace distributio
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 Discussio
Chapter 3 Outlier Detection and Robust Mixture Modeling Using Nonconvex Penalized Likelihood
3．1 Introductio
3．2 Robust Mixture Model via Mean-Shift Penalizatio
3．2．1 RMM for Equal Component Variances
3．2．2 RMM for Unequal Component Variances
3．2．3 Tuning Parameter Selectio
3．3 Simulatio
3．3．1 Methods and Evaluation Measures
3．3．2 Results
3．4 Real Data Applicatio
3．5 Discussio
Chapter 4 Outlier Detection and Robust Mixture Regression Using Nonconvex Penalized Likelihood
4．1 Introductio
4．2 Robust Mixture Regression via Mean-shift Penalizatio
4．3 Simulatio
4．3．1 Simulation Setups
4．3．2 Methods and Evaluation Measures
4．3．3 Results
4．4 Tone Perception Data Analysis
4．5 Discussio
Appendix
References