Statistical inference for partially hidden Markov models

  title={Statistical inference for partially hidden Markov models},
  author={Laurent and L Pierre}
In this paper we introduce a new missing data model, based on a standard parametric Hidden Markov Model (HMM), for which informations on the latent Markov chain are given since this one reaches a fixed state (and until it leaves this state). We study, under mild conditions, the consistency and asymptotic normality of the maximum likelihood estimator. We point out also that the underlying Markov chain does not need to be ergodic, and that identifiability of the model is not tractable in a simple… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications referenced by this paper.
Showing 1-10 of 33 references

Statistique de châınes de Markov cachées à espace d’états fini

D. Bakry, X. Milhaud, P. Vandekerkhove
Le cas non stationnaire. C. R. Acad. Sci. Paris, Série I, 325, 203–206. • 1997
View 5 Excerpts
Highly Influenced

Monte Carlo EM estimation in time series models involving counts

K. S. Chan, J. Ledolter
J. Am. Statist. Assoc. 90, No. 429, 242–252. • 1995
View 4 Excerpts
Highly Influenced

Multiscale Image Segmentation Using Wavelet-Domain Hidden Markov Models

2008 4th International Conference on Wireless Communications, Networking and Mobile Computing • 2008
View 1 Excerpt

On the convergence of the Monte Carlo maximum likelihood method for latent variable models

O. Cappé, R. Douc, E. Moulines, C. Robert
Scand. J. Satistit. 29, 615–635. • 2002
View 1 Excerpt

Asymptotics of the Maximum Likelihood Estimator for generalHidden Markov

View 1 Excerpt

Similar Papers

Loading similar papers…