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dc.contributor.authorHung, Yingen_US
dc.date.accessioned2008-09-17T19:31:07Z
dc.date.available2008-09-17T19:31:07Z
dc.date.issued2008-05-19en_US
dc.identifier.urihttp://hdl.handle.net/1853/24707
dc.description.abstractThis thesis consists of two parts. The first part focuses on design and analysis for computer experiments and the second part deals with binary time series and its application to kinetic studies in micropipette experiments. The first part of the thesis addresses three problems. The first problem is concerned with optimal design of computer experiments. Latin hypercube designs (LHDs) have been used extensively for computer experiments. A multi-objective optimization approach is proposed to find good LHDs by combining correlation and distance performance measures. Several examples are presented to show that the obtained designs are good in terms of both criteria. The second problem is related to the analysis of computer experiments. Kriging is the most popular method for approximating complex computer models. Here a modified kriging method is proposed, which has an unknown mean model. Therefore it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented which show remarkable improvement in prediction using blind kriging over ordinary kriging. The third problem is related to computer experiments with nested and branching factors. Design and analysis of experiments with branching and nested factors are challenging and have not received much attention in the literature. Motivated by a computer experiment in a machining process, we develop optimal LHDs and kriging methods that can accommodate branching and nested factors. Through the application of the proposed methods, optimal machining conditions and tool edge geometry are attained, which resulted in a remarkable improvement in the machining process. The second part of the thesis deals with binary time series analysis with application to cell adhesion frequency experiments. Motivated by the analysis of repeated adhesion tests, a binary time series model incorporating random effects is developed in this chapter. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. Application of the proposed methodology to real data from a T-cell experiment reveals some interesting information. These results provide some quantitative evidence to the speculation that cells can have ¡§memory¡¨ in their adhesion behavior.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectRandom effect modelen_US
dc.subjectKrigingen_US
dc.subjectBinary time seriesen_US
dc.subjectComputer experimentsen_US
dc.subjectLatin hypercube designen_US
dc.subjectDesign of experimentsen_US
dc.subject.lcshDigital computer simulation
dc.subject.lcshExperimental design
dc.subject.lcshTime-series analysis
dc.subject.lcshBinary system (Mathematics)
dc.subject.lcshMicropipettes
dc.subject.lcshKriging
dc.subject.lcshCell adhesion
dc.titleContributions to computer experiments and binary time seriesen_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentIndustrial and Systems Engineeringen_US
dc.description.advisorCommittee Chair: C. F. Jeff Wu; Committee Co-Chair: Roshan Joseph Vengazhiyil; Committee Member: Kwok L. Tsui; Committee Member: Ming Yuan; Committee Member: Shreyes N. Melkoteen_US


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