Special Colloquium
Huixia (Judy) Wang
University of Illinois at Urbana-Champaign
Inference for Quantile Regression Models with Applications to GeneChip Data
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.
Monday February 20, 2006 at 4:00 PM in SEO 636