Variable Selection Methods for Population-based Genetic Association Studies: SPLS and HSIC

Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/9165

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DC Field	Value	Language
dc.contributor.advisor	Balakrishnan, Narayanaswamy	en_US
dc.contributor.advisor	Xie, Changchun	en_US
dc.contributor.author	Qin, Maochang	en_US
dc.date.accessioned	2014-06-18T16:45:54Z	-
dc.date.available	2014-06-18T16:45:54Z	-
dc.date.created	2011-05-31	en_US
dc.date.issued	2011	en_US
dc.identifier.other	opendissertations/4312	en_US
dc.identifier.other	5330	en_US
dc.identifier.other	2039571	en_US
dc.identifier.uri	http://hdl.handle.net/11375/9165	-
dc.description.abstract	<p>This project aims to identify the single nucleotide polymorphisms(SNPs), which are associated with the muscle size and strength in Caucasian. Two methods sparse partial least squares (SPLS) and sparse Hilbert-Schmidt independence criterion (HSIC) were applied for dimension reduction and variables selection in the Functional SNPs Associated with Muscle Size and Strength(FAMuss) Study. The selection ability of two methods was compared by simulations. The genetic determinants of skeletal muscle size and strength before and after exercise training in Caucasian were selected by using these two methods.</p>	en_US
dc.subject	Statistics and Probability	en_US
dc.subject	Statistics and Probability	en_US
dc.title	Variable Selection Methods for Population-based Genetic Association Studies: SPLS and HSIC	en_US
dc.type	thesis	en_US
dc.contributor.department	Statistics	en_US
dc.description.degree	Master of Science (MS)	en_US
Appears in Collections:	Open Access Dissertations and Theses

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