Statistics and Data Science Seminar
Prof. Dabao Zhang
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
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.
Wednesday September 9, 2009 at 3:00 PM in SEO 636