Non parametric finite translation mixtures with dependent regime

Elisabeth Gassiat (1) , Judith Rousseau (2, 3)

1 LM-Orsay - Laboratoire de Mathématiques d'Orsay
2 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
3 CREST - Centre de Recherche en Économie et Statistique

In this paper we consider non parametric finite translation mixtures. We prove that all the parameters of the model are identifiable as soon as the matrix that defines the joint distribution of two consecutive latent variables is non singular and the translation parameters are distinct. Under this assumption, we provide a consistent estimator of the number of populations, of the translation parameters and of the distribution of two consecutive latent variables, which we prove to be asymptotically normally distributed under mild dependency assumptions. We propose a non parametric estimator of the unknown translated density. In case the latent variables form a Markov chain (Hidden Markov models), we prove an oracle inequality leading to the fact that this estimator is minimax adaptive over regularity classes of densities.

Mots clés: translation mixtures; non parametric estimation; semi-parametric models; Hidden Markov models; dependent latent variable models

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Elisabeth Gassiat, Judith Rousseau. Non parametric finite translation mixtures with dependent regime. 2013. ⟨hal-00786750⟩