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Bayesian estimation and prediction with multiply Type-II censored samples of sequential order statistics from one- and two-parameter exponential distributions

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Title: Bayesian estimation and prediction with multiply Type-II censored samples of sequential order statistics from one- and two-parameter exponential distributions
Authors: Schenk, N.1, Burkschat, M.2 marco.burkschat@ovgu.de, Cramer, E.3, Kamps, U.3
Source: Journal of Statistical Planning & Inference. Apr2011, Vol. 141 Issue 4, p1575-1587. 13p.
Subjects: Bayes' estimation, Sequential analysis, Exponential families (Statistics), Density functionals, Numerical analysis, Parameter estimation, Order statistics
Abstract: Abstract: Based on multiply Type-II censored samples of sequential order statistics, Bayesian estimators are derived for the parameters of one- and two-parameter exponential distributions. In the one-parameter set-up, the posterior density is obtained under the assumption that the prior distribution is given by an inverse Gamma distribution, and the Bayes estimator with respect to squared error loss is calculated. Its performance is illustrated by a numerical example and compared with two non-Bayesian estimators, namely the BLUE and the approximate maximum likelihood estimator (AMLE). Moreover, prediction of future failure times is considered. Minimum risk equivariant estimators and predictors are deduced from the given results. Finally, similar results are presented for the two-parameter situation. [Copyright &y& Elsevier]
Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 56497973
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Statistical+Planning+%26+Inference%22">Journal of Statistical Planning & Inference</searchLink>. Apr2011, Vol. 141 Issue 4, p1575-1587. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Bayes'+estimation%22">Bayes' estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+analysis%22">Sequential analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Exponential+families+%28Statistics%29%22">Exponential families (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Density+functionals%22">Density functionals</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Order+statistics%22">Order statistics</searchLink>
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  Data: Abstract: Based on multiply Type-II censored samples of sequential order statistics, Bayesian estimators are derived for the parameters of one- and two-parameter exponential distributions. In the one-parameter set-up, the posterior density is obtained under the assumption that the prior distribution is given by an inverse Gamma distribution, and the Bayes estimator with respect to squared error loss is calculated. Its performance is illustrated by a numerical example and compared with two non-Bayesian estimators, namely the BLUE and the approximate maximum likelihood estimator (AMLE). Moreover, prediction of future failure times is considered. Minimum risk equivariant estimators and predictors are deduced from the given results. Finally, similar results are presented for the two-parameter situation. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jspi.2010.11.009
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        Text: English
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        PageCount: 13
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      – SubjectFull: Bayes' estimation
        Type: general
      – SubjectFull: Sequential analysis
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      – SubjectFull: Exponential families (Statistics)
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      – SubjectFull: Density functionals
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      – SubjectFull: Numerical analysis
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      – SubjectFull: Parameter estimation
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      – SubjectFull: Order statistics
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              Text: Apr2011
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