2026-09-20T12:56:08Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/87408 2023-07-11 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

He, Xuming Wang, Huixia 2015-09-28T16:02:44Z 2015-09-28T16:02:44Z 10000-01-01 2006 2006 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. Made available in DSpace on 2015-09-28T16:02:44Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3243019.pdf: 2895039 bytes, checksum: 1fb84b386b865a4f428ad91693f3199d (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 88689 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 114 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/87408 (MiAaPQ)AAI3243019 eng Statistics Inference on Quantile Regression for Mixed Models With Applications to GeneChip Data text Statistics Statistics University of Illinois at Urbana-Champaign Dissertation Ph.D.