Rank test for heteroscedastic functional data

• Published in: Journal of multivariate analysis 2010-09, Vol.101 (8), p.1791-1805
• Main Authors: Wang, Haiyan, Akritas, Michael G.
• Format: Article
• Language: English
• Subjects: Asymptotic methods; Exact sciences and technology; Global analysis, analysis on manifolds; High-dimensional multivariate analysis; Hypothesis testing; Mathematics; Measurement; Multivariate analysis; Nonparametric inference; Parametric inference; Probability and statistics; Repeated measures; Repeated measures Nonparametric inference Hypothesis testing High-dimensional multivariate analysis; Sciences and techniques of general use; Simulation; Statistics; Studies; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds
• ISSN: 0047-259X; 1095-7243
• DOI: 10.1016/j.jmva.2010.03.012

In this paper, we consider (mid-)rank based inferences for testing hypotheses in a fully nonparametric marginal model for heteroscedastic functional data that contain a large number of within subject measurements from possibly only a limited number of subjects. The effects of several crossed factors and their interactions with time are considered. The results are obtained by establishing asymptotic equivalence between the rank statistics and their asymptotic rank transforms. The inference holds under the assumption of α -mixing without moment assumptions. As a result, the proposed tests are applicable to data from heavy-tailed or skewed distributions, including both continuous and ordered categorical responses. Simulation results and a real application confirm that the (mid-)rank procedures provide both robustness and increased power over the methods based on original observations for non-normally distributed data.