Inference on Quantile Regression for Mixed Models With Applications to GeneChip Data
Wang, Huixia
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Permalink
https://hdl.handle.net/2142/87408
Description
Title
Inference on Quantile Regression for Mixed Models With Applications to GeneChip Data
Author(s)
Wang, Huixia
Issue Date
2006
Doctoral Committee Chair(s)
He, Xuming
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Statistics
Language
eng
Abstract
The proposed test is motivated by studies of GeneChip data to identify differentially expressed genes through the analysis of probe level measurements. Realizing that the number of replicates is usually small in GeneChip studies, we propose a genome-wide adjustment to the test statistic to account for within-array correlation and several enhanced quantile approaches by borrowing information across genes. 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.
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