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Estimating the transition probabilities from censored Markov renewal processes

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  • Phelan, Michael J.

Abstract

The problem of estimating the transition probabilities for the Markov chain associated to a Markov renewal process is considered. The estimators are to be based on censored observations of the Markov renewal process. For a class of Markov renewal processes whose transition distributions factor, nonparametric estimators are defined. They are shown to be consistent and to converge weakly go Gaussian random variables. The result builds upon those in Gill (1980) for nonparametric estimation in this setting.

Suggested Citation

  • Phelan, Michael J., 1990. "Estimating the transition probabilities from censored Markov renewal processes," Statistics & Probability Letters, Elsevier, vol. 10(1), pages 43-47, June.
    • Handle: RePEc:eee:stapro:v:10:y:1990:i:1:p:43-47
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    Cited by:

    1. Richard L. Warr & Travis B. Woodfield, 2020. "Bayesian nonparametric estimation of first passage distributions in semi‐Markov processes," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(2), pages 237-250, March.
    2. Joseph Gardiner & Andrew Hogan & Margaret Holmes-Rovner & David Rovner & Lawrence Griffith & Joel Kupersmith, 1995. "Confidence Intervals for Cost - Effectiveness Ratios," Medical Decision Making, , vol. 15(3), pages 254-263, August.

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