Thursday, September 3, 2009
04:30 PM in MATH 175
Professor Dabao Zhang
Department of Statistics, Purdue University

Penalized orthogonal-components regression for large p small n data

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 response residuals. A new penalization framework, implemented via empirical Bayes thresholding, is presented to effectively identify sparse predictors of each component. POCRE is computationally efficient owing to its sequential 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 a joint work with Yanzhu Lin and Min Zhang.

Refreshments will be served at 4:00 PM in HAAS 111.