Reliability estimation in a generalized life-model with application to the Burr-XII

Estimators of the reliability function in a GLM (generalized life model) are considered. The class of the GLM includes (among others) the Weibull, Pareto, Beta, Gompertz, and Rayleigh distribution. A proper general prior density and the predictive function for general class of distribution proposed...

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Bibliographic Details
Published inIEEE transactions on reliability Vol. 51; no. 3; pp. 337 - 343
Main Author Soliman, A.A.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Approximation
Bayesian analysis
Computer simulation
Density
Distribution functions
Entropy
Estimates
Estimators
Life estimation
Mathematical models
Maximum likelihood estimation
Monte Carlo methods
Performance loss
Predictive models
Probability density function
Reliability theory
Shape
Testing
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Summary:Estimators of the reliability function in a GLM (generalized life model) are considered. The class of the GLM includes (among others) the Weibull, Pareto, Beta, Gompertz, and Rayleigh distribution. A proper general prior density and the predictive function for general class of distribution proposed by Al-Hussaini(1999) are used to obtain the exact estimate. Also, the Bayes estimates relative to symmetric loss function (quadratic loss), and asymmetric loss function (LINEX loss, and GE loss), are obtained. Comparisons are made between those estimators and the MLE applying to the Burr-XII model using the Bayes approximation due to Lindley. Monte Carlo simulation was used.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2002.801855