主讲人简介：

Weixin Yao（姚卫鑫），美国加州大学河滨分校统计系副教授（终身）、研究生主管，美国宾州州立大学统计系博士，国际统计协会当选会员。现担任4个国际顶级统计期刊副主编，包括Biometrics, Journal of Computational and Graphical Statistics, Journal of Multivariate Analysis and The American Statistician。主要研究方向包括混合模型、高维数据、稳健数据分析、纵向数据分析、非参和半参模型。其科研项目多次获美国自然科学基金（NSF）和美国国家能源局（DOE）资助。关于纵数回归的文章获2012年度非参统计杂志最佳文章奖。

内容摘要：

Built on the ideas of mean and quantile, mean regression and quantile regression are extensively investigated and popularly used to model the relationship between a dependent variable Y and covariates x. However, the research about the regression model built on the mode is rather limited. In this talk, we propose a new regression tool, named modal regression, that aims to find the most probable conditional value (mode) of a dependent variable Y given covariates x rather than the mean that is used by the traditional meanregression. The modal regression can reveal new interesting data structure that is possibly missed by the conditional mean or quantiles. In addition, modal regression is resistent to outliers and heavy tailed data, and can provide shorter prediction intervals when the data are skewed. Furthermore, unlike traditional mean regression, the modal regression can be directly applied to the truncated data. Modal regression could be a potentially very useful regression tool that can complement the traditional mean and quantile regressions.

报告主持：胡良剑

报告语言：英语