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Modeling and Inference for Multi-state Systems

In: Recent Advances in Multi-state Systems Reliability

Author

Listed:
  • Vlad Stefan Barbu

    (Université de Rouen)

  • Alex Karagrigoriou

    (University of the Aegean)

Abstract

In this work we are focused on multi-state systems modeled by means of a special type of semi-Markov processes. The sojourn times are seen to be independent not necessarily identically distributed random variables and assumed to belong to a general class of distributions closed under extrema that includes, in addition to some discrete distributions, several typical reliability distributions like the exponential, Weibull, and Pareto. A special parametrization is proposed for the parameters describing the system, taking thus into account various types of dependencies of the parameters on the the states of the system. We obtain maximum likelihood estimators of the parameters and plug-in type estimators are furnished for the basic quantities describing the semi-Markov system under study.

Suggested Citation

  • Vlad Stefan Barbu & Alex Karagrigoriou, 2018. "Modeling and Inference for Multi-state Systems," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel & Alex Karagrigoriou (ed.), Recent Advances in Multi-state Systems Reliability, pages 59-70, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-319-63423-4_4
    DOI: 10.1007/978-3-319-63423-4_4
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    Cited by:

    1. Guglielmo D’Amico & Giovanni Villani, 2021. "Valuation of R&D compound option using Markov chain approach," Annals of Finance, Springer, vol. 17(3), pages 379-404, September.

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