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Non‐homogeneous continuous‐time Markov and semi‐Markov manpower models

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  • Sally McClean
  • Erin Montgomery
  • Fidelis Ugwuowo

Abstract

We develop estimation methods for continuous‐time Markov and semi‐Markov non‐homogeneous manpower systems using the notion of calendar time divided into ‘time windows’ by change points. The model parameters may only change at these change points but remain constant between them. Our estimation methods employ a competing risks approach and allow for left truncated and right censored data. Maximum likelihood estimators are given for the hazard and survivor functions describing length of stay in any grade of the manpower system. The models are fitted to data from the Northern Ireland nursing service. © 1998 John Wiley & Sons, Ltd.

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  • Sally McClean & Erin Montgomery & Fidelis Ugwuowo, 1997. "Non‐homogeneous continuous‐time Markov and semi‐Markov manpower models," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(3‐4), pages 191-198, September.
    • Handle: RePEc:wly:apsmda:v:13:y:1997:i:3-4:p:191-198
    DOI: 10.1002/(SICI)1099-0747(199709/12)13:3/43.0.CO;2-T
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    Cited by:

    1. Brecht Verbeken & Marie-Anne Guerry, 2021. "Discrete Time Hybrid Semi-Markov Models in Manpower Planning," Mathematics, MDPI, vol. 9(14), pages 1-13, July.
    2. Sally McClean & Lingkai Yang, 2023. "Semi-Markov Models for Process Mining in Smart Homes," Mathematics, MDPI, vol. 11(24), pages 1-16, December.

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