Nonparametric finite translation hidden Markov models and extensions
Élisabeth Gassiat, Judith Rousseau · Bernoulli · 2015
In this paper, we consider nonparametric finite translation hidden Markov models, or more generally finite translation mixtures with dependent latent variables. 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 nonparametric estimator of the unknown translated density. In case the latent variables form a Markov chain, we prove that this estimator is minimax adaptive over regularity classes of densities.