Hypothesis testing under mixture models: Application to genetic linkage analysis

Kung Yee Lian, Paul J. Rathouz

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we propose a new class of statistics to test a simple hypothesis against a family of alternatives characterized by a mixture model. Unlike the likelihood ratio statistic, whose large sample distribution is still unknown in this situation, these new statistics have a simple asymptotic distribution to which to refer under the null hypothesis. Simulation results suggest that it has adequate power in detecting the alternatives. Its application to genetic linkage analysis in the presence of the genetic heterogeneity that motivated this work is emphasized.

Original languageEnglish (US)
Pages (from-to)65-74
Number of pages10
JournalBiometrics
Volume55
Issue number1
DOIs
StatePublished - Mar 1999

Keywords

  • Chi-squared distribution
  • Genetic linkage analysis
  • Hypothesis testing
  • Likelihood ratio statistic
  • Score function

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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