Empirical likelihood-based inference for genetic mixture models

Chiung Yu Huang, Jing Qin, Fei Zou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The authors show how the genetic effect of a quantitative trait locus can be estimated by a non-parametric empirical likelihood method when the phenotype distributions are completely unspecified. They use an empirical likelihood ratio statistic for testing the genetic effect and obtaining confidence intervals. In addition to studying the asymptotic properties of these procedures, the authors present simulation results and illustrate their approach with a study on breast cancer resistance genes.

Original languageEnglish (US)
Pages (from-to)563-574
Number of pages12
JournalCanadian Journal of Statistics
Issue number4
StatePublished - Dec 2007
Externally publishedYes


  • Empirical likelihood
  • Interval mapping
  • Nonparametric model
  • Normal mixture model
  • Profile likelihood
  • Quantitative trait loci

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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