Maximum likelihood estimator for hidden Markov models in continuous time

Pavel Chigansky*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The paper studies large sample asymptotic properties of the Maximum Likelihood Estimator (MLE) for the parameter of a continuous time Markov chain, observed in white noise. Using the method of weak convergence of likelihoods due to Ibragimov and Khasminskii (Statistical estimation, vol 16 of Applications of mathematics. Springer-Verlag, New York), consistency, asymptotic normality and convergence of moments are established for MLE under certain strong ergodicity assumptions on the chain.

Original languageAmerican English
Pages (from-to)139-163
Number of pages25
JournalStatistical Inference for Stochastic Processes
Volume12
Issue number2
DOIs
StatePublished - Jun 2009

Keywords

  • Continuous time hidden Markov models
  • Filtering
  • Maximum Likelihood estimator
  • Partial observations

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