报告时间：2017年1月7日（星期五）上午10:00
报告地点：科学会堂A602
报告摘要：It is known that the outliers impact more heavily on mixture linear regression models than on the usual linear regression models, since the outliers not only affect the estimation of the regression parameters, but also possibly totally blur the mixture structure. A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture of a normal and a latent distribution, this procedure is implemented by an EM algorithm which incorporates two types of missing information from the mixture class membership and the latent variable. Finite sample performance of the proposed algorithm is evaluated by simulations. The proposed method is compared with other procedures, and a sensitivity study is also conducted based on a real data set.
报告人简介：宋卫星，男，博士，美国堪萨斯州立大学统计系教授兼研究生项目主任，博士生导师，山西省“百人计划”人才。1999年在中国科学院系统科学研究所获第一个博士学位，1999-2001在北京师范大学数学系做博士后，2006年在美国密歇根州立大学获得第二个博士学位。曾在香港大学精算与统计系和美国罗切斯特大学生物统计系做访问学者。主要从事测量误差模型、非参数与半参数模型中的统计推断，高维数据分析与大数据建模，稳健性估计研究。主持或参与多项自然基金项目和联合项目的研究工作，目前主持一项美国国家自然科学基金项目，发表SCI检索论文40余篇。担任国际十几种主要统计学期刊的长期审稿人。
