c3cc7d371d96d310VgnVCM100000c2b1d38dRCRDapproved/UMICH/stats/Home/News & Events/Archived Events/2011-2012 EventsToshiya Hoshikawa###@###(Tue, 24 Apr 2012)Toshiya Hoshikawa###@###(Tue, 24 Apr 2012)438 West HallContributions to Functional Data Analysis and High-Throughput Screening Assay Analysisstats1335276000000133527600000010:00 AM<p><b>Title:</b> Contributions to functional data analysis and high-throughput screening assay analysis<br> <b>Co-Chairs:</b> Professor Tailen Hsing, Professor Kerby Shedden<br> <b>Cognate Member: </b>Professor Bin Nan<br> <b>Member:</b> Professor Naisyin Wang</p> <p><b>Abstract: </b>The first half of the talk explores mixture regression, a method to cluster a sample and estimate each regression model for the clusters simultaneously. This method treats the covariate as deterministic so that it carries no information as to the membership of the subject. Although this assumption may be reasonable in experiments, in observational data the covariate usually behaves differently across the groups. To accommodate the method to incorporate the covariate heterogeneity, we introduce joint mixture regression. The method is developed for both the multivariate covariate and the functional covariate. We explore joint mixture regression analytically and numerically and present a real-data example where this new approach performs better than the traditional approach. The second half of the talk explores high throughput screening (HTS) assay analysis. HTS assays can be used as less expensive alternatives to conventional animal and cell culture assays. In this context, a prediction relationship between the HTS and conventional assays must be defined. In some applications, the lowest value among the conventional assays is of primary interest, in which case it may be advantageous to predict this minimum value directly rather than in two stages following prediction of each assay separately. We explore an approach that focuses the modeling efforts directly on the parameter of interest, rather than on the high dimensional nuiscance parameter. We apply this method to the ToxCast data of the EPA and to the 60 cell line screen of the NCI.</p>Njjsantosjjsantos136682986444793cc7d371d96d310VgnVCM100000c2b1d38d____once11112newnewEvent Flyer/UMICH/stats/Home/Events/Dissertations and Oral Preliminary Examinations/Toshiya Hoshikawa Defense Flyer.pdfToshiya Hoshikawa