PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Asymptotic distribution and power of the likelihood ratio test for mixtures: bounded and unbounded case.
Elisabeth Gassiat, Jean-Marc Azais and Cecile Mercadier
Bernoulli 2004.

Abstract

we consider the log-likelihood ratio test (LRT) for testing the number of components in a mixture of populations in a parametric family. We provide the asymptotic distribution of the LRT statistic under the null hypothesis as well as under contiguous alternatives when the parameter set is bounded. Moreover, for the simple contamination model we prove that, under general assumptions, the asymptotic power under contiguous hypotheses may be arbitrarily close to the asymptotic level when the set of parameters is large enough. In the particular problem of normal distributions, we prove that, when the unknown mean is not a priori bounded, the asymptotic power under contiguous hypotheses is equal to the asymptotic level.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:672
Deposited By:Elisabeth Gassiat
Deposited On:29 December 2004