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Teoriya Veroyatnostei i ee Primeneniya, 1997, Volume 42, Issue 1, Pages 169–178
DOI: https://doi.org/10.4213/tvp1718 (Mi tvp1718) 		 
This article is cited in 7 scientific papers (total in 7 papers)

Maximum likelihood estimator and Kullback–Leibler information in misspecified Markov chain models

P. E. Greenwooda, W. Wefelmeyerb

a University of British Columbia
b University of Siegen
Full-text PDF (555 kB) Citations (7)
DOI: https://doi.org/10.4213/tvp1718
Abstract: Suppose we have specified a parametric model for the transition distribution of a Markov chain, but the true transition distribution does not belong to the model. Then the maximum likelihood estimator estimates the parameter which maximizes the Kullback–Leibler information between the true transition distribution and the model. We prove that the maximum likelihood estimator is asymptotically efficient in a nonparametric sense if the true transition distribution is unknown.
Keywords: efficient estimation, Kullback–Leibler information, Markov chain, maximum likelihood estimator, incorrect model.
Received: 31.10.1995
English version:
Theory of Probability and its Applications, 1998, Volume 42, Issue 1, Pages 103–111
DOI: https://doi.org/10.1137/S0040585X9797599X
Bibliographic databases:
Language: English
Citation: P. E. Greenwood, W. Wefelmeyer, “Maximum likelihood estimator and Kullback–Leibler information in misspecified Markov chain models”, Teor. Veroyatnost. i Primenen., 42:1 (1997), 169–178; Theory Probab. Appl., 42:1 (1998), 103–111
Citation in format AMSBIB
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\paper Maximum likelihood estimator and Kullback--Leibler information in misspecified Markov chain models
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\transl
\jour Theory Probab. Appl.
\yr 1998
\vol 42
\issue 1
\pages 103--111
\crossref{https://doi.org/10.1137/S0040585X9797599X}
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  • https://www.mathnet.ru/eng/tvp/v42/i1/p169
    • This publication is cited in the following 7 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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    		Теория вероятностей и ее применения Theory of Probability and its Applications
     
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