Title: Contributions to functional data analysis and high-throughput screening assay analysis
Co-Chairs: Professor Tailen Hsing, Professor Kerby Shedden
Cognate Member: Professor Bin Nan
Member: Professor Naisyin Wang
Abstract: 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.