Skip to Main content Skip to Navigation
Journal articles

Maximum likelihood estimation for general hidden semi-Markov processes with backward recurrence time dependence

Abstract : This article concerns the study of the asymptotic properties of the maximum likelihood estimator (MLE) for the general hidden semi-Markov model (HSMM) with backward recurrence time dependence. By transforming the general HSMM into a general hidden Markov model, we prove that under some regularity conditions, the MLE is strongly consistent and asymptotically normal. We also provide useful expressions for the asymptotic covariance matrices, involving the MLE of the conditional sojourn times and the embedded Markov chain of the hidden semi-Markov chain.
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/inria-00576524
Contributor : Samis Trevezas Connect in order to contact the contributor
Submitted on : Monday, March 14, 2011 - 3:33:11 PM
Last modification on : Sunday, June 26, 2022 - 10:03:19 AM
Long-term archiving on: : Wednesday, June 15, 2011 - 3:15:08 AM

File

Gen_HSMM_final.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Samis Trevezas, Nikolaos Limnios. Maximum likelihood estimation for general hidden semi-Markov processes with backward recurrence time dependence. Journal of Mathematical Sciences, Springer Verlag (Germany), 2009, 163 (3), pp.262-274. ⟨10.1007/s10958-009-9675-9⟩. ⟨inria-00576524⟩

Share

Metrics

Record views

61

Files downloads

237