Proportional hazards models with discrete frailty

We extend proportional hazards frailty models for lifetime data to allow a negative binomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test. Zero frailty corresponds to a limited failure...

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Bibliographic Details
Published in:Lifetime data analysis 2010-07, Vol.16 (3), p.374-384
Main Authors: Caroni, Chrys, Crowder, Martin, Kimber, Alan
Format: Article
Language:English
Subjects:
Accelerated life tests
Binomial distribution
Data Interpretation, Statistical
Economics
Electronics - standards
Failure
Finance
Frailty
Hazards
Health Sciences
Humans
Insurance
Kaplan-Meier Estimate
Lifetime
Management
Mathematics
Mathematics and Statistics
Medicine
Operations Research/Decision Theory
Probability distribution
Proportional Hazards Models
Quality Control
Random variables
Reliability
Safety and Risk
Software reliability
Statistics
Statistics for Business
Statistics for Life Sciences
Studies
Variance analysis
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Summary:We extend proportional hazards frailty models for lifetime data to allow a negative binomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test. Zero frailty corresponds to a limited failure model containing a proportion of units that never fail (long-term survivors). Ways of modifying the model to avoid this are discussed. The models are illustrated on a previously published set of data on failures of printed circuit boards and on new data on breaking strengths of samples of cord.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-010-9151-3