University of Georgia
Brooks Hall, Room 327

Large and complex data have been generated routinely from various sources, for instance, time course biological studies and social media. Classic nonparametric models, such as smoothing spline ANOVA models, are not well equipped to analyze such large and complex data. To overcome these challenges, I propose novel nonparametric methods under a reproducing kernel Hilbert space framework to (1) significantly reduce daunting computational costs of selecting smoothing parameters for smoothing spline ANOVA models; (2) model the data with a functional response and a functional predictor; (3) accurately identify differentially expressed genes in time course RNA-seq data. To validate my proposed methods, I conduct simulation studies and apply the methods to real data in time course biological studies and social media.