Bootstrap tests for multivariate directional alternatives

Abu T M Minhajuddin, William H. Frawley, William R. Schucany, Wayne A. Woodward

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Tests on multivariate means that are hypothesized to be in a specified direction have received attention from both theoretical and applied points of view. One of the most common procedures used to test this cone alternative is the likelihood ratio test (LRT) assuming a multivariate normal model for the data. However, the resulting test for an ordered alternative is biased in that the only usable critical values are bounds on the null distribution. The present paper provides empirical evidence that bootstrapping the null distribution of the likelihood ratio statistic results in a bootstrap test (BT) with comparable power properties without the additional burden of assuming multivariate normality. Additionally, the tests based on the LRT statistic can reject the null hypothesis in favor of the alternative even though the true means are far from the alternative region. The BT also has similar properties for normal and nonnormal data. This anomalous behavior is due to the formulation of the null hypothesis and a possible remedy is to reformulate the null to be the complement of the alternative hypothesis. We discuss properties of a BT for the modified set of hypotheses (MBT) based on a simulation study. The resulting test is conservative in general and in some specific cases has power estimates comparable to those for existing methods. The BT has higher sensitivity but relatively lower specificity, whereas the MBT has higher specificity but relatively lower sensitivity.

Original languageEnglish (US)
Pages (from-to)2302-2315
Number of pages14
JournalJournal of Statistical Planning and Inference
Issue number7
StatePublished - Jul 1 2007


  • Cone
  • Correlated data
  • Likelihood ratio
  • Mean
  • Nonparametric
  • One-sided
  • Ordered
  • Positive orthant

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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