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Sequential Estimation Functions in Stochastic Population Processes

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Mathematical Statistics and Probability Theory

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Abstract

Sequential, i.e., randomly stopped estimation functions in a class of continuous time stochastic population models are considered. The class includes finite, irreducible Markov processes. Three types of efficient sequential estimation functions are discussed and their asymptotic behaviour is investigated. The main tools of analysis are taken from point process theory.

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Authors and Affiliations

  1. Institute of Mathematics, University of Munich, Theresienstr. 39, D-8000, Munich 2, West Germany

    Helmut Pruscha

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  1. Helmut Pruscha
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Editor information

Editors and Affiliations

  1. University of Cologne, Germany

    P. Bauer

  2. University of Agriculture, Vienna, Austria

    F. Konecny

  3. Technical University, Vienna, Austria

    W. Wertz

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© 1987 by D. Reidel Publishing Company, Dordrecht, Holland

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Pruscha, H. (1987). Sequential Estimation Functions in Stochastic Population Processes. In: Bauer, P., Konecny, F., Wertz, W. (eds) Mathematical Statistics and Probability Theory. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3965-3_18

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  • DOI: https://doi.org/10.1007/978-94-009-3965-3_18

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-8259-4

  • Online ISBN: 978-94-009-3965-3

  • eBook Packages: Springer Book Archive

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