Journal ArticleDOI
Asymptotic distribution of the likelihood ratio test that a mixture of two binomials is a single binomial
Herman Chernoff,Eric S. Lander +1 more
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TLDR
In this paper, it was shown that the asymptotic distribution of a mixture of two binomial distributions with the Kullback Leibler information grows stochastically as log k.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 1995-01-01. It has received 107 citations till now. The article focuses on the topics: Asymptotic distribution & Binomial distribution.read more
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Testing the number of components in a normal mixture
TL;DR: In this article, it was shown that the likelihood ratio statistic based on the Kullback-Leibler information criterion of the null hypothesis that a random sample is drawn from a k 0 -component normal mixture distribution against the alternative hypothesis that the sample was drawn from an k 1 -component normalized mixture distribution is asymptotically distributed as a weighted sum of independent chi-squared random variables with one degree of freedom, under general regularity conditions.
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Genetic dissection of complex traits
TL;DR: This article synthesizes the current state of the genetic dissection of complex traits--describing the methods, limitations, and recent applications to biological problems.
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Sensitivity and specificity of information criteria.
TL;DR: In some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC.
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A modified likelihood ratio test for homogeneity in finite mixture models
TL;DR: In this article, a modified LRT for homogeneity in finite mixture models with a general parametric kernel distribution family is proposed, which has a X2-type of null limiting distribution and is asymptotic most powerful under local alternatives.
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Testing the order of a model using locally conic parametrization : population mixtures and stationary ARMA processes
TL;DR: This paper addresses the problem of testing hypotheses using the likelihood ratio test statistic in nonidentifiable models, with application to model selection in situations where the parametrization for the larger model leads to nonidentifiability in the smaller model.
References
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The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
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Extremes and Related Properties of Random Sequences and Processes.
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On the Distribution of the Likelihood Ratio
TL;DR: In this paper, the asymptotic distribution of the likelihood ratio λ is examined when the value of the parameter is a boundary point of both the set of points corresponding to the hypothesis and the set corresponding to an alternative.
On the asymptotic performance of the log likelihood ratio statistic for the mixture model and related results
TL;DR: In this paper, the authors developed an asymptotic theory of the log likelihood ratio test statistic for testing homogeneity (i.e., no mixture) against mixture alternatives.