主题：New Regression Model: Modal Regression
Weixin Yao，教授、博士生导师，加州大学河滨分校统计系副主任。主要研究混合模型、非参数和半参数建模、稳健数据分析和高维建模等。曾担任《Biometrics》《Journal of Computational and Graphical Statistics》《Journal of Multivariate Analysis》和《The American Statistician》等多家著名期刊的副主编及《Advances in Data Analysis and Classification》期刊客座主编。在Journal of the American Statistics Association, Journal of the Royal Statistical Society, Ser B, Journal of Economics等SCI期刊发表论文90余篇。
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 resistant 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.