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Parametric Model Discrimination for Heavily Censored Survival Data

Leemis, Lawrence
; 
Block, A. D.
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 & Oakes 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 nonparametric survival function estimate.
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2008-06-01
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Mathematics
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Parametric_Model_Discrimination_for_Heavily_Censored_Survival_Data_Leemis_2008.pdf
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Mathematics
DOI
https://doi.org/10.1109/TR.2008.923488
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https://scholarworks.wm.edu/handle/internal/1064
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