Rank test for heteroscedastic functional data

Haiyan Wang, M. G. Akritas Michael G.

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1791-1805
Number of pages15
JournalJournal of Multivariate Analysis
Volume101
Issue number8
DOIs
StatePublished - Sep 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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