Methods for high-dimensional multivariate and multi-group repeated measures data under non-normality

Solomon W. Harrar, John Z. Hossler

Research output: Contribution to journalArticlepeer-review


Asymptotic tests for multivariate repeated measures are derived under non-normality and unspecified dependence structure. Notwithstanding their broader scope of application, the methods are particularly useful when a random vector of large number of repeated measurements are collected from each subject but the number of subjects per treatment group is limited. In some experimental situations, replicating the experiment large number of times could be expensive or infeasible. Although taking large number of repeated measurements could be relatively cheaper, due to within subject dependence the number of parameters involved could get large pretty quickly. Under mild conditions on the persistence of the dependence, we have derived asymptotic multivariate tests for the three testing problems in repeated measures analysis. The simulation results provide evidence in favour of the accuracy of the approximations to the null distributions.

Original languageEnglish
Pages (from-to)1056-1074
Number of pages19
Issue number5
StatePublished - Sep 2 2016

Bibliographical note

Publisher Copyright:
© 2016 Taylor & Francis.


  • alpha-mixing
  • asymptotics
  • growth curve
  • multivariate tests
  • robust methods

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Methods for high-dimensional multivariate and multi-group repeated measures data under non-normality'. Together they form a unique fingerprint.

Cite this