### Penalized orthogonal-components regression for large p small n data

- Series
- Stochastics Seminar
- Time
- Thursday, August 27, 2009 - 15:00 for 1 hour (actually 50 minutes)
- Location
- Skiles 269
- Speaker
- Dabao Zhang – Purdue University

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 an joint work with Yanzhu Lin and Min Zhang