Non parametric finite translation mixtures with dependent regime
Rousseau, Judith; Gassiat, Elisabeth (2013), Non parametric finite translation mixtures with dependent regime. https://basepub.dauphine.fr/handle/123456789/11033
Type
Document de travail / Working paperExternal document link
http://hal.archives-ouvertes.fr/hal-00786750Date
2013Publisher
Unviersité Paris-Dauphine
Published in
Paris
Pages
26
Metadata
Show full item recordAbstract (EN)
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.Subjects / Keywords
dependent latent variable models; Hidden Markov models; semi-parametric models; non parametric estimation; translation mixturesRelated items
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