Parametric Model Discrimination for Heavily Censored Survival Data

A. Daniel Block and Lawrence M. Leemis ()
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A. Daniel Block: Block Consulting, Inc.
Lawrence M. Leemis: The College of William and Mary

Chapter 14 in Computational Probability Applications, 2017, pp 191-215 from Springer

Abstract: 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.

Keywords: Competing risks; Distribution selection; Kernel functions; Probability (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-43317-2_14

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DOI: 10.1007/978-3-319-43317-2_14

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