A0291
Title: Hypothesis testing in high dimensions
Authors: Ming-Yen Cheng - Hong Kong Baptist University (Hong Kong) [presenting]
Abstract: High dimensionality is one of the key features in big data, and it poses many new challenges in the analysis and inference. Some hypothesis testing problems will be discussed occurring when we make inference based on different types of high-dimensional data. In particular, the focus will be on analysis of variance with functional data, two-sample testing with multivariate data, and goodness-of-fit testing for some semiparametric regression models. These problems have their own challenging issues, and we will present some feasible approach(es) to each of them. Theoretical and numerical results, and applications to datasets coming from medical and climate studies will be given to demonstrate the efficacy and advantages of the proposed methods. Some future research directions will be discussed.
