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Auxiliary Results

  • Yu. A. Kutoyants
Part of the Lecture Notes in Statistics book series (LNS, volume 134)

Abstract

A Poisson process on metric space is introduced and some of the properties of the stochastic integral with respect to this process are described. This integral allows us to define the likelihood ratio formula and to derive certain useful inequalities for the moments of likelihood ratio. Supposing that the intensity function of the Poisson process depends on the unknown finite-dimensional parameter, we define the maximum likelihood, Bayes, and minimum distance estimators of this parameter and give the first examples of these estimators.

Keywords

Poisson Process Fisher Information Intensity Function Mathematical Expectation Auxiliary Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Yu. A. Kutoyants
    • 1
  1. 1.Laboratoire de Statistique et ProcessusUniversité du MaineLe MansFrance

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