TY - JOUR
T1 - Maximum likelihood estimator for hidden Markov models in continuous time
AU - Chigansky, Pavel
PY - 2009/6
Y1 - 2009/6
N2 - 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.
AB - 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.
KW - Continuous time hidden Markov models
KW - Filtering
KW - Maximum Likelihood estimator
KW - Partial observations
UR - http://www.scopus.com/inward/record.url?scp=67650681118&partnerID=8YFLogxK
U2 - 10.1007/s11203-008-9025-4
DO - 10.1007/s11203-008-9025-4
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AN - SCOPUS:67650681118
SN - 1387-0874
VL - 12
SP - 139
EP - 163
JO - Statistical Inference for Stochastic Processes
JF - Statistical Inference for Stochastic Processes
IS - 2
ER -