• Bernoulli
  • Volume 22, Number 1 (2016), 193-212.

Nonparametric finite translation hidden Markov models and extensions

Elisabeth Gassiat and Judith Rousseau

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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.

Article information

Bernoulli Volume 22, Number 1 (2016), 193-212.

Received: June 2013
Revised: December 2013
First available in Project Euclid: 30 September 2015

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dependent latent variable models hidden Markov models nonparametric estimation translation mixtures


Gassiat, Elisabeth; Rousseau, Judith. Nonparametric finite translation hidden Markov models and extensions. Bernoulli 22 (2016), no. 1, 193--212. doi:10.3150/14-BEJ631.

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Supplemental materials

  • Supplement to “Nonparametric finite translation hidden Markov models and extensions”. In the supplementary material, we provide an oracle inequality which is used to prove Theorem 4, together with the proofs of the oracle inequality and of Theorem 4. We also give a concentration inequality which is used in various parts of these proofs.