Modeling multiple risks in the presence of double censoring

Self-consistent (SC) iterative algorithms will be proposed to non-parametrically estimate the cause-specific cumulative incidence functions in a multiple decrement, doubly censored context. Double censoring is defined to include both left and right censored observations, in addition to exact observa...

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Bibliographic Details
Published in:Scandinavian actuarial journal 2010-03, Vol.2010 (1), p.68-81
Main Author: Adamic, Peter F.
Format: Article
Language:English
Subjects:
Actuarial science
Algorithms
Masking
Mathematical models
Multiple decrement
Risk assessment
Self-consistency
Studies
Survival function
Turnbull's algorithm
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Summary:Self-consistent (SC) iterative algorithms will be proposed to non-parametrically estimate the cause-specific cumulative incidence functions in a multiple decrement, doubly censored context. Double censoring is defined to include both left and right censored observations, in addition to exact observations. The algorithms are a generalization of the classical univariate algorithms of Efron and Turnbull. Unlike any previous competing risk models proposed in the literature to date, the proposed algorithms will be fully non-parametric while also explicitly allowing for the possibility of masked modes of failure, whereby failure is known only to occur due to a subset from the set of all possible causes. In short, the method is useful in any actuarial application that encounters censored and/or masked risks. The paper concludes by showing how the method can be applied to employee benefits modeling.
ISSN:0346-1238
1651-2030
DOI:10.1080/03461230802420603