 On empirical likelihood for a semiparametric mixture modelF. Zou Biometrika, 2002, vol. 89, issue 1, 61-75 Abstract: Plant and animal studies of quantitative trait loci provide data which arise from mixtures of distributions with known mixing proportions. Previous approaches to estimation involve modelling the distributions parametrically. We propose a semiparametric alternative which assumes that the log ratio of the component densities satisfies a linear model, with the baseline density unspecified. It is demonstrated that a constrained empirical likelihood has an irregularity under the null hypothesis that the two densities are equal. A factorisation of the likelihood suggests a partial empirical likelihood which permits unconstrained estimation of the parameters, and which is shown to give consistent and asymptotically normal estimators, regardless of the null. The asymptotic null distribution of the log partial likelihood ratio is chi-squared. Theoretical calculations show that the procedure may be as efficient as the full empirical likelihood in the regular set-up. The usefulness of the robust methodology is illustrated with a rat study of breast cancer resistance genes. Copyright Biometrika Trust 2002, Oxford University Press. Date: 2002 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:89:y:2002:i:1:p:61-75 Ordering information: This journal article can be ordered from Access Statistics for this article Biometrika is currently edited by Paul Fearnhead More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. |
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