B0371
Title: High-dimensional MANOVA via bootstrapping and its application to functional and sparse count data
Authors: Miles Lopes - UC Davis (United States) [presenting]
Zhenhua Lin - National University of Singapore (Singapore)
Hans-Georg Mueller - University of California Davis (United States)
Abstract: A new approach is proposed for the problem of high-dimensional multivariate ANOVA via bootstrapping max statistics. The proposed method is suited to simultaneously test the equality of several pairs of mean vectors of potentially more than two populations. By exploiting the ``variance decay'' property that is a natural feature in relevant applications, we are able to establish a dimension-free and nearly parametric convergence rate for bootstrap approximation. In addition, we illustrate the proposed approach with ANOVA problems for functional data and sparse count data. The proposed method is shown to work well in simulations and several real data applications.
