A semiparametric extension of the Mann-Whitney test for randomly truncated data

Warren B. Bilker, Mei Cheng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

In many applications, statistical data are frequently observed subject to a retrospective sampling criterion resulting in pure right-truncated data. In classical testing problems, the Mann-Whitney test is used for testing the equality of two distributions. A semiparametric extension of this test is developed for the case when truncation is present. We consider a model in which the truncation distribution is parameterized, while the lifetime distribution is left as a nonparametric component. The method is seen to be applicable to many patterns of truncation including left truncation, right truncation, and doubly truncated data for which no other nonparametric or semiparametric test is currently available. Applications of the semiparametric method are given. Simulation results indicate that for pure right-truncated data the semiparametric test is more powerful than a recent nonparametric test.

Original languageEnglish (US)
Pages (from-to)10-20
Number of pages11
JournalBiometrics
Volume52
Issue number1
DOIs
StatePublished - Mar 1 1996

Keywords

  • Retrospective sampling
  • Selection bias
  • Semiparametric model
  • Test statistic
  • Truncation

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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