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Parametric Model Discrimination for Heavily Censored Survival Data

In: Computational Probability Applications

Author

Listed:
  • A. Daniel Block

    (Block Consulting, Inc.)

  • Lawrence M. Leemis

    (The College of William and Mary)

Abstract

Abstract Simultaneous discrimination among various parametric lifetime models is an important step in the parametric analysis of survival data. We consider a plot of the skewness versus the coefficient of variation for the purpose of discriminating among parametric survival models. We extend the method of Cox and Oakes (1984, Analysis of Survival Data, Chapman & Hall/CRC)from complete to censored data by developing an algorithm based on a competing risks model and kernel function estimation. A by-product of this algorithm is a non-parametric survival function estimate.

Suggested Citation

  • A. Daniel Block & Lawrence M. Leemis, 2017. "Parametric Model Discrimination for Heavily Censored Survival Data," International Series in Operations Research & Management Science, in: Andrew G. Glen & Lawrence M. Leemis (ed.), Computational Probability Applications, chapter 14, pages 191-215, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-43317-2_14
    DOI: 10.1007/978-3-319-43317-2_14
    as

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