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dc.contributor.advisor Muller, Peter
dc.creatorRossell, David
dc.date.accessioned 2009-06-03T21:13:09Z
dc.date.available 2009-06-03T21:13:09Z
dc.date.issued 2007
dc.identifier.urihttps://hdl.handle.net/1911/20638
dc.description.abstract This thesis consists of three projects. The first project introduces methodology to design drug development studies in an optimal fashion. Optimality is defined in a decision-theoretic framework where the goal is expected utility maximization. We show how our approach outperforms some other conventional designs. The second project generalizes the hierarchical Gamma/Gamma model for microarray data analysis. We illustrate how our generalization improves the fit without increasing the model complexity, and how one can use it to find differentially expressed genes and to build a classifier. When the sample size is small our method finds more genes and classifies samples better than several standard methods. Only as the number of microarrays grows large competing methods detect more genes. The last project explores the use of L 2E partial density estimation as an exploratory technique in the context of microarray data analysis. We propose a heuristic that combines frequentist and Bayesian ideas. Our approach outperforms other competing methods when its assumptions hold, but it presents increased false positive rates when the assumptions do not hold.
dc.format.extent 107 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectStatistics
dc.title Some approaches to Bayesian design of experiments and microarray data analysis
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Rossell, David. "Some approaches to Bayesian design of experiments and microarray data analysis." (2007) Diss., Rice University. https://hdl.handle.net/1911/20638.


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