Title
Inference for Quantile Regression Models with Applications to GeneChip Data
Speaker
Huixia Wang, University of Illinois at Urbana-Champaign
Abstract
The traditional inference for the linear mixed models depends strongly on the normality assumption, which is easily violated in some applications. We develop a robust rank score test for linear quantile models with a random effect. The rank score test can be carried out at a single quantile level or jointly at several quantile levels. It is derived for homoscedastic error models, but is valid for inference on treatment effects in an important class of mixed models with heteroscedastic errors.
The proposed test is motivated by studies of GeneChip data to identify differentially expressed genes through the analysis of probe level measurements. We propose a genome-wide adjustment to the test statistic to account for within-array correlation, and demonstrate that the proposed test is highly effective even when the number of arrays is small. Our empirical studies of GeneChip data show that inference on the quartiles of the gene expression distribution is a valuable complement to the usual mixed model analysis based on Gaussian likelihood.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
