Empirical likelihood-based inference for genetic mixture models

Chiung Yu Huang, Jing Qin, Fei Zou

Research output: Contribution to journalArticle

Abstract

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
Volume35
Issue number4
StatePublished - Dec 2007
Externally publishedYes

Fingerprint

Empirical Likelihood
Mixture Model
Nonparametric Likelihood
Quantitative Trait Loci
Likelihood Ratio Statistic
Likelihood Methods
Breast Cancer
Phenotype
Asymptotic Properties
Confidence interval
Gene
Testing
Simulation
Inference
Mixture model
Empirical likelihood
Resistance
Likelihood ratio statistic
Breast cancer
Asymptotic properties

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Empirical likelihood-based inference for genetic mixture models. / Huang, Chiung Yu; Qin, Jing; Zou, Fei.

In: Canadian Journal of Statistics, Vol. 35, No. 4, 12.2007, p. 563-574.

Research output: Contribution to journalArticle

Huang, CY, Qin, J & Zou, F 2007, 'Empirical likelihood-based inference for genetic mixture models', Canadian Journal of Statistics, vol. 35, no. 4, pp. 563-574.
Huang, Chiung Yu ; Qin, Jing ; Zou, Fei. / Empirical likelihood-based inference for genetic mixture models. In: Canadian Journal of Statistics. 2007 ; Vol. 35, No. 4. pp. 563-574.
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