Efficient semiparametric estimation and model selection for multidimensional mixtures

被引:5
|
作者
Gassiat, Elisabeth [1 ]
Rousseau, Judith [2 ]
Vernet, Elodie [3 ]
机构
[1] Univ Paris Saclay, Univ Paris Sud, CNRS, Lab Math Orsay, F-91405 Orsay, France
[2] Univ Paris 09, CEREMADE, Paris, France
[3] Ecole Polytech, CMAP, Route Saclay, F-91128 Palaiseau, France
来源
ELECTRONIC JOURNAL OF STATISTICS | 2018年 / 12卷 / 01期
关键词
Semiparametric statistics; mixture models; efficiency; Bernstein von Mises theorem; VON MISES THEOREM; LATENT-STRUCTURE MODELS; HIDDEN MARKOV-MODELS; DECOMPOSITIONS; DISTRIBUTIONS;
D O I
10.1214/17-EJS1387
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
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页码:703 / 740
页数:38
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