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Efficient semiparametric estimation and model selection for multidimensional mixtures

Gassiat, Elisabeth; Rousseau, Judith; Vernet, Elodie (2018), Efficient semiparametric estimation and model selection for multidimensional mixtures, Electronic Journal of Statistics, 12, 1, p. 703-740. 10.1214/17-EJS1387

Type
Article accepté pour publication ou publié
External document link
https://hal.archives-ouvertes.fr/hal-01345919
Date
2018
Journal name
Electronic Journal of Statistics
Volume
12
Number
1
Publisher
Institute of Mathematical Statistics
Pages
703-740
Publication identifier
10.1214/17-EJS1387
Metadata
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Author(s)
Gassiat, Elisabeth
Rousseau, Judith
Vernet, Elodie
Abstract (EN)
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 procedure 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.
Subjects / Keywords
semiparametric statistics; mixture models; efficiency; Bernstein von Mises Theorem

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