学 术 报 告
报告题目：Envelopes and partial least squares regression
报告者单位：University of Florida
摘要：We build connections between envelopes, a recently proposed context for efficient estimation in multivariate statistics, and multivariate partial least squares (PLS) regression. In particular, we establish an envelope as the nucleus of both univariate and multivariate PLS, which opens the door to pursuing the same goals as PLS but using different envelope estimators. It is argued that a likelihood-based envelope estimator is less sensitive to the number of PLS components selected and that it outperforms PLS in prediction and estimation.
报告人简介：Su is associate Professor of Department of Statistics of University of Florida. Her primary research interests fall in a new area at the intersection of multivariate analysis and dimension reduction, called envelopes, which was introduced by Cook, Li and Chiaromonte (2010) under the framework of multivariate linear regression. It applies dimension reduction techniques to remove the immaterial information and achieve efficient estimation of the regression coefficients. The envelope method has broad application in many fields in multivariate analysis such as discriminant analysis, functional data analysis, reduced rank regression and sufficient dimension reduction. It can also be connected to other fields in statistics, including partial least squares, model selection, Bayesian statistics, experimental design, and time series.