学术讲座预告：New Regression Models Based on the Mode
题目：报告人：姚卫鑫
时间：2019年7月4日9:40
地点：博远楼805
内容简介：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 mean regression. 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.
主讲人简介：
姚卫鑫（Weixin Yao）是美国加州大学河滨分校统计系副教授（终身）和研究生主管，美国宾州州立大学统计系博士，国际统计协会当选会员。他现在担任四个国际顶级统计期刊的副主编包括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。他的科研项目多次获得了美国自然科学基金（NSF）和 美国国家能源局（DOE）的资助。他的关于纵数回归的文章获得了2012年度非参统计杂志最佳文章奖。
