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Statistical Estimation in Hierarchical Hidden Markov Model

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Cybernetics and Systems Analysis Aims and scope

Abstract

The mathematical model of a stochastic hierarchical structure is presented and several problems of statistical estimation in case of incomplete observations are considered. A method is outlined to construct consistent estimators of parameters for hidden Markov model using the structure of correlation dependence of Markov chains. Such models often occur in applied fields of theory of stochastic processes such as queuing theory, inventory control theory, risk theory, etc. The paper contains specific examples of parametric estimation for models in the above-mentioned scientific domains.

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Authors and Affiliations

  1. Koszalin University of Technology (Politechnika Koszalinska), Koszalin, Poland

    A. A. Voina

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  1. A. A. Voina
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Correspondence to A. A. Voina.

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Translated from Kibernetika i Sistemnyi Analiz, No. 6, November–December, 2014, pp. 87–103.

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Voina, A.A. Statistical Estimation in Hierarchical Hidden Markov Model. Cybern Syst Anal 50, 898–912 (2014). https://doi.org/10.1007/s10559-014-9681-x

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  • DOI: https://doi.org/10.1007/s10559-014-9681-x

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