A Bayesian method for finding interactions.

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dc.contributor.author Chen, Wei
dc.contributor.advisor Ghosh, Debashis
dc.contributor.advisor Raghunathan, Trivellore E.
dc.date.accessioned 2016-08-30T16:08:30Z
dc.date.available 2016-08-30T16:08:30Z
dc.date.issued 2006
dc.identifier.uri http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:3237929
dc.identifier.uri http://hdl.handle.net/2027.42/126120
dc.description.abstract In genomic studies, datasets with a small sample size and a large number of potential predictors are common. Recently, gene-gene interactions (epistasis) and gene-environment interactions have been drawing increasing attention due to the etiology of complex diseases. If all possible pair wise interactions are to be explored, then this leads to a high dimensional model space. There is very little work to handle this common problem. The emphasis of my research is on selecting interactions and controlling the number of falsely discovered predictors with a limited sample size. The method I propose simultaneously satisfies the two properties for inclusion of interactions: interpretability and discovery. In addition, I develop a novel equivalence between variable selection procedures and the false discovery rate. One application of my research is the development of a model to aid the therapeutic decision by identifying prognostic factors or interactions among abundant variables from the clinical and molecular profiles of patients. Given a patient's profile, an optimal treatment involves a trade-off between efficacy and toxicity. My research also proposes a novel way to compare treatments with multiple endpoints.
dc.format.extent 115 p.
dc.language English
dc.language.iso EN
dc.subject Bayesian
dc.subject False Discovery
dc.subject Finding
dc.subject Interactions
dc.subject Method
dc.subject Sample Size
dc.title A Bayesian method for finding interactions.
dc.type Thesis
dc.description.thesisdegreename Ph.D.
dc.description.thesisdegreediscipline Biological Sciences
dc.description.thesisdegreediscipline Biostatistics
dc.description.thesisdegreegrantor University of Michigan, Horace H. Rackham School of Graduate Studies
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/126120/2/3237929.pdf
dc.owningcollname Dissertations and Theses (Ph.D. and Master's)
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