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Title:Inference on Quantile Regression for Mixed Models With Applications to GeneChip Data
Author(s):Wang, Huixia
Doctoral Committee Chair(s):He, Xuming
Department / Program:Statistics
Discipline:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Statistics
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.
Issue Date:2006
Type:Text
Language:English
Description:114 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
URI:http://hdl.handle.net/2142/87408
Other Identifier(s):(MiAaPQ)AAI3243019
Date Available in IDEALS:2015-09-28
Date Deposited:2006


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