Open Access
2018 Efficient semiparametric estimation and model selection for multidimensional mixtures
Elisabeth Gassiat, Judith Rousseau, Elodie Vernet
Electron. J. Statist. 12(1): 703-740 (2018). DOI: 10.1214/17-EJS1387


In this paper, we consider nonparametric multidimensional finite mixture models and we are interested in the semiparametric estimation of the population weights. Here, the i.i.d. observations are assumed to have at least three components which are independent given the population. We approximate the semiparametric model by projecting the conditional distributions on step functions associated to some partition. Our first main result is that if we refine the partition slowly enough, the associated sequence of maximum likelihood estimators of the weights is asymptotically efficient, and the posterior distribution of the weights, when using a Bayesian procedure, satisfies a semiparametric Bernstein-von Mises theorem. We then propose a cross-validation like method to select the partition in a finite horizon. Our second main result is that the proposed procedure satisfies an oracle inequality. Numerical experiments on simulated data illustrate our theoretical results.


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Elisabeth Gassiat. Judith Rousseau. Elodie Vernet. "Efficient semiparametric estimation and model selection for multidimensional mixtures." Electron. J. Statist. 12 (1) 703 - 740, 2018.


Received: 1 September 2016; Published: 2018
First available in Project Euclid: 27 February 2018

zbMATH: 06864474
MathSciNet: MR3769193
Digital Object Identifier: 10.1214/17-EJS1387

Primary: 62G05 , 62G20

Keywords: Bernstein von Mises theorem , efficiency , Mixture models , semiparametric statistics

Vol.12 • No. 1 • 2018
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