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Identifiability Crises in Competing Risks

Name: Identifiability Crises in Competing Risks
Creator: Martin Crowder
Keywords: Reliability and life testing, reliability, latent failure times, 05 social sciences, review, identifiability, 01 natural sciences, Applications of statistics to biology and medical sciences; meta analysis, survival analysis, 0502 economics and business

Identifiability crises in competing risks

descriptionPublicationkeyboard_double_arrow_right Article 01 Dec 1994Publisher:JSTORJournal:International Statistical Review / Revue Internationale de Statistique, volume 62, page 379 (issn: 0306-7734,

Authors: Martin Crowder;

doi: 10.2307/1403768

Identifiability Crises in Competing Risks

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Abstract

Summary: Difficulties and pitfalls of dependency modelling in Statistics are very well illustrated by problems of identifiability in Competing Risks. This paper gives a review of such problems with examples intended to animate the theoretical results. The problems covered arise through the traditional way of modelling Competing Risks via latent failure times.

Keywords

Reliability and life testing, reliability, latent failure times, review, identifiability, Applications of statistics to biology and medical sciences; meta analysis, survival analysis, specified marginals, regression, dependence models, competing risks

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	34
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average