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Nonparametric predictive inference for combined competing risks data.

Coolen-Maturi, T. and Coolen, F.P.A. (2014) 'Nonparametric predictive inference for combined competing risks data.', Reliability engineering & system safety., 126 . pp. 87-97.


The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies.

Item Type:Article
Keywords:Imprecise probability, Lower and upper probability, Nonparametric predictive inference, Competing risks, Right-censored data, Combined data.
Full text:(AM) Accepted Manuscript
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Publisher statement:NOTICE: this is the author’s version of a work that was accepted for publication in Reliability Engineering & System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering & System Safety, 126, 2014, 10.1016/j.ress.2014.01.007.
Date accepted:12 January 2014
Date deposited:04 June 2014
Date of first online publication:24 January 2014
Date first made open access:No date available

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