講座題目：New Regression Model: Modal Regression
主 講 人：加州大學河濱分校姚衛鑫教授
講座時間：2023年12月14日（周四）15：00-16：00
講座地點：6號學院樓402會議室
主辦單位：新葡萄8883官網AMG、浙江省2011“數據科學與大數據分析協同創新中心”
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 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.
主講人簡介：
姚衛鑫，教授、博士生導師，加州大學河濱分校統計系副主任。主要研究混合模型、非參數和半參數建模、穩健數據分析和高維建模等。曾擔任《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編及《Advances in Data Analysis and Classification》期刊客座主編。在Journal of the American Statistics Association, Journal of the Royal Statistical Society, Ser B, Journal of Economics等SCI期刊發表論文90余篇。
歡迎感興趣的師生積極參加！
