A test statistic with a ranking method based on the Jeffreys divergence measure

Hidetoshi Murakami, Soshi Kawada

Research output: Contribution to journalArticlepeer-review

Abstract

Nonparametric two-sample tests are important statistical procedures in many scientific fields. The test statistic has been derived from the Kullback–Liebler divergence between two empirical distribution functions. This study modifies a nonparametric two-sample test statistic using a ranking method. The modified test statistic is shown to be based on the Jeffreys divergence measure. The exact critical value of the proposed test statistic is derived for small sample sizes. Simulations are used to investigate the power of the proposed test statistic for the location alternative and for any difference between distributions, with various population distributions, for small sample sizes.

Original languageEnglish
Pages (from-to)266-279
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume51
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • Jeffreys divergence measure
  • Kullback-Liebler divergence measure
  • power comparison
  • ranking method

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