Title: "Sufficient Dimension Reduction in High-dimensional Data"
Speaker: Dr. Lexin Li, Department of Biochemistry and Molecular Medicine, University of California, Davis
Place: Smith Hall (SMTH) 108; Tuesday, March 1, 2005, 4:30pm

Abstract

Given a large number of predictors in a regression, it is often desirable to reduce the dimensionality of the problem by replacing the original high-dimensional data with a low-dimensional space composed of a few key predictors or linear combinations of predictors. In this talk, I will first introduce the general framework of sufficient dimension reduction (SDR), which targets the reduction of dimension without losing any information on the conditional distribution of response given predictors, and without pre-specifying any parametric model. Two specific works within the SDR framework will be examined. The first is a model-free variable selection approach, which identifies contributing predictors prior to any model formulation. The second is the application of SDR to a microarray survival data analysis, where the goal is to predict the patients' survival time using gene expression profiles. Some related SDR methodological work and genomic studies will also be briefly reviewed.


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