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Asymptotic inference for continuous-time Markov chains
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  • Published: December 1988

Asymptotic inference for continuous-time Markov chains

  • R. Höpfner1 

Probability Theory and Related Fields volume 77, pages 537–550 (1988)Cite this article

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Summary

This paper deals with asymptotic optimal inference in a time-continuous ergodic Markov chain with countable state space, based on observation of the process up to timet. Let the infinitesimal generator depend on an unknown parameter. Under weak assumptions on the parametrization, we show local asymptotic normality for the statistical model ast→∞. As a consequence, limit distributions of sequences of competing estimators for the unknown parameter are more spread out than a specified normal distribution.

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Authors and Affiliations

  1. Institut für Mathematische Stochastik, Albert-Ludwig-Universität, Hebelstrasse 27, D-7800, Freiburg, Germany

    R. Höpfner

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  1. R. Höpfner
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Höpfner, R. Asymptotic inference for continuous-time Markov chains. Probab. Th. Rel. Fields 77, 537–550 (1988). https://doi.org/10.1007/BF00959616

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  • Received: 20 June 1986

  • Revised: 23 October 1987

  • Issue Date: December 1988

  • DOI: https://doi.org/10.1007/BF00959616

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Keywords

  • Normal Distribution
  • Statistical Model
  • Markov Chain
  • State Space
  • Stochastic Process
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