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Estimation and tests in finite mixture modelsof nonparametric densities

Published online by Cambridge University Press:  04 July 2009

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Abstract

The aim is to study the asymptotic behavior of estimators and testsfor the components of identifiable finite mixture models ofnonparametric densities with a known number of components.Conditions for identifiability of the mixture components andconvergence of identifiable parameters are given.The consistency and weak convergence of the identifiable parametersand test statistics are presented for several models.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2009

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References

Azaïs, J.M., Gassiat, E. and Mercadier, C., Asymptotic distribution and local power of the likelihood ratio test for mixtures: bounded and unbounded cases. Bernoulli 12 (2006) 775799. CrossRef
K. Fukumizu, Likelihood ratio of unidendifiable models and multilayer neural networks, Ann. Statist. 31 (2003) 833–851.
J.K. Ghosh and P.K. Sen, On the aymptotic performance of the log-likelihood ratio statistic for the mixture model and related results. In Proc. of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer, volume II, edited by L.M. Le Cam and R.A. Olshen, (1985), pp. 789–806.
M. Lemdani and O. Pons, Likelihood ratio tests in mixture models. C. R. Acad. Sci. Paris, Ser. I 322 (1995) 399–404.
M. Lemdani and O. Pons, Likelihood ratio tests in contamination models, Bernoulli 5 (1999) 705–719.
X. Liu and Y. Shao, Asymptotics for likelihood ratio tests under loss of identifiability, Ann. Statist. 31 (2003) 807–832.
O. Pons, Estimation et tests dans les modèles de mélanges de lois et de ruptures. Hermès-Science Lavoisier, London and Paris (2009).
A. Wald, Note on the consistency of the maximum likelihood estimate, Ann. Math. Statist. 20 (1949) 595–601.