讲座题目 | Envelope-based partial least squares | ||
主办单位 | 数理与统计学院 | 协办单位 | 应用统计系 |
讲座时间 | 6月22日14:00-15:00 | 主讲人 | Zhihua Su副教授 |
讲座地点 | 行政楼1308室 | ||
主讲人简介 | Zhihua Su is Associate Professor in Department of Statistics at the University of Florida. She got her PhD in 2012 from University of Minnesota, Twin Cities. Her research interest includes Feature selection, Dimension reduction, Multivariate analysis, Bayesian statistics, Algorithm development, Software development, Applied statistics. | ||
讲座内容简介 | Partial least squares (PLS) is widely used in applied sciences an alternative method to ordinary least squares (OLS) for estimating the regression coefficients. It is known that PLS often has a better prediction performance compared to OLS, and the PLS algorithms can be adapted directly to the n < p case. Despite its popularity, the theoretical properties of the PLS estimator are largely unknown. As a result, it is hard to determine when PLS is better than OLS, what are the limitations for PLS and how to improve PLS. Cook et al. (2013) built a connection between PLS with a dimension reduction method called the envelope model. They showed that at the population level, PLS and the envelope model have the same target parameter, but they use different algorithms for estimation. This connection allows PLS to be studied in a traditional likelihood framework and facilitates model developments. We will address three issues of PLS in this context: variable selection, categorical predictors and scale invariance. | ||