Per citar aquest document: http://ddd.uab.cat/record/97721
Stress-strength reliability of Weibull distribution based on progressively censored samples
Asgharzadeh, Akbar
Valiollahi, Reza
Raqab, Mohammad Z.

Data: 2011
Resum: Based on progressively Type-II censored samples, this paper deals with inference for the stress-strength reliability R = P(Y < X) when X and Y are two independent Weibull distributions with different scale parameters, but having the same shape parameter. The maximum likelihood estimator, and the approximate maximum likelihood estimator of R are obtained. Different confidence intervals are presented. The Bayes estimator of R and the corresponding credible interval using the Gibbs sampling technique are also proposed. Further, we consider the estimation of R when the same shape parameter is known. The results for exponential and Rayleigh distributions can be obtained as special cases with different scale parameters. Analysis of a real data set as well a Monte Carlo simulation have been presented for illustrative purposes.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: article ; recerca ; publishedVersion
Matèria: Maximum likelihood estimator ; Approximate maximum likelihood estimator ; Bootstrap confidence interval ; Bayesian estimation ; Metropolis-Hasting method ; Progressive Type-II censoring
Publicat a: SORT : statistics and operations research transactions, Vol. 35, Núm. 2 (July-December 2011) , p. 103-124, ISSN 1696-2281



22 p, 208.0 KB
 Accés restringit a la UAB

El registre apareix a les col·leccions:
Articles > Articles publicats > SORT : Statistics and Operations Research Transactions
Articles > Articles de recerca

 Registre creat el 2012-07-24, darrera modificació el 2016-08-03



   Favorit i Compartir