Permutation tests using least distance estimator in the multivariate regression model

Sooncheol Sohn, Byoung Cheol Jung, Myoungshic Jhun

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper proposes two permutation tests based on the least distance estimator in a multivariate regression model. One is a type of t test statistic using the bootstrap method, and the other is a type of F test statistic using the sum of distances between observed and predicted values under the full and reduced models. We conducted a simulation study to compare the power of the proposed permutation tests with that of the parametric tests based on the least squares estimator for three types of hypotheses in several error distributions. The results indicate that the power of the proposed permutation tests is greater than that of the parametric tests when the error distribution is skewed like the Wishart distribution, has a heavy tail like the Cauchy distribution, or has outliers.

Original languageEnglish
Pages (from-to)191-201
Number of pages11
JournalComputational Statistics
Volume27
Issue number2
DOIs
StatePublished - Jun 2012

Keywords

  • Bootstrap estimator
  • Least distance estimator
  • Least squares estimator
  • Permutation test

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