Prof. Dabao Zhang : Penalized orthogonal-components regression for large p small n data

Posted by , part of the Statistics and Data Science Seminar.

At

Sept. 9, 2009, 3 p.m.

In

SEO 636

Abstract

We propose a penalized orthogonal-components regression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the re- sponse residuals. A new penalization framework, implemented via empiri- cal Bayes thresholding, is presented to effectively identify sparse predictors of each component. POCRE is computationally efficient owing to its se- quential construction of leading sparse principal components. In addition, such construction offers other properties such as grouping highly correlated predictors and allowing for collinear or nearly collinear predictors. With multivariate responses, POCRE can construct common components and thus build up latent-variable models for large p small n data. This is an joint work with Yanzhu Lin and Min Zhang.