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Estimation for incomplete manpower data

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  • S. I. McClean
  • J. O. Gribbin

Abstract

Much attention has been paid to both non‐parametric and parametric estimation for survival data with right censoring, particularly in the medical literature. In manpower planning the completed length of service until leaving is of great interest, and here also the data are right censored since people are still in service when data collection ends. However, it often occurs that the data are also left truncated since people are already in service at the beginning of data collection. These people have often been neglected both in estimation of the empirical distribution function and also in fitting particular parametric distributions. However, it is important to include them so as to use all the data, particularly when data are only present for a short period. The methods developed were applied to data for the completed length of service of both skilled and unskilled workers where the data were collected over a period of years. Using modified Kaplan‐Meier estimation, applied to these data sets, empirical distribution functions were obtained. A number of parametric distributions were also fitted. The goodness of fit of these distributions as predictors of leavers and stayers over a given period was then tested using a chi‐squared test.

Suggested Citation

  • S. I. McClean & J. O. Gribbin, 1987. "Estimation for incomplete manpower data," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 3(1), pages 13-25.
  • Handle: RePEc:wly:apsmda:v:3:y:1987:i:1:p:13-25
    DOI: 10.1002/asm.3150030103
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    Cited by:

    1. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    2. Brecht Verbeken & Marie-Anne Guerry, 2021. "Discrete Time Hybrid Semi-Markov Models in Manpower Planning," Mathematics, MDPI, vol. 9(14), pages 1-13, July.
    3. P.-C. G. Vassiliou & T. P. Moysiadis, 2010. "$\boldsymbol{\mathcal{G}-}$ Inhomogeneous Markov Systems of High Order," Methodology and Computing in Applied Probability, Springer, vol. 12(2), pages 271-292, June.
    4. P.-C.G. Vassiliou, 2020. "Non-Homogeneous Semi-Markov and Markov Renewal Processes and Change of Measure in Credit Risk," Mathematics, MDPI, vol. 9(1), pages 1-27, December.

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