Statistics and Data Science Seminar

Prof. Min Zhang
Purdue University
Cancelled
Abstract: High throughput biotechnologies such as microarray and next-generation sequencing permit simultaneous measurements of enormous bodies of expression and sequence information. However, the number of biological samples is much smaller compared to the number of available predictors. Statistically, we are challenged by the large number of parameters but small number of observations. To tackle this issue, we proposed a two-step variable selection procedure to reduce the dimension in the first stage where Gibbs sampler was developed to stochastically search through low-dimensional subspaces. With reduced number of variables, either Bayesian variable selection or traditional approaches can be employed in the second stage. The methods are evaluated via simulation studies and we also applied them to real data sets, including QTL mapping data and gene expression data.
Wednesday December 3, 2008 at 4:15 PM in SEO 612
Web Privacy Notice HTML 5 CSS FAE
UIC LAS MSCS > persisting_utilities > seminars >