On Bayesian predictive distributions of generalized order statistics

AL-Hussaini, Essam K.; Ahmad, Abd EL-Baset A.

doi:10.1007/s001840200207

Cited by 60 publications

(33 citation statements)

References 29 publications

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“…For more details, see AL-Hussaini and Ahmad (2003), Ali Mousa (2003 and AL-Hussaini (2004). Recently, a few of authors utilized the GOS's in Bayesian inference.…”

Section: −T α and H(·) = F (·)mentioning

confidence: 99%

“…Ahsanullah (1996;2000), Kamps and Gather (1997), Cramer and Kamps (2000), Habibullah and Ahsanullah (2000), Jaheen (2002;2005a), AL-Hussaini and Ahmad (2003), AL-Hussaini (2004), Ahmad (2007;2008), Aboeleneen (2010), Abushal (2006-2010), ), Abu El Fotouh (2011 and Ateya and Ahmad (2011) among others, utilized the (GOS's) in their Works.…”

Section: −T α and H(·) = F (·)mentioning

confidence: 99%

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Bayesian Prediction Based on Generalized Order Statistics from a Mixture of Two Exponentiated Weibull Distribution Via MCMC Sumulation

Abushal¹,

AL-Zaydi²

2012

IJSP

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This paper is concerned with the problem of obtaining the maximum likelihood prediction (point and interval) and Bayesian prediction (point and interval) for a future observation from mixture of two exponentiated Weibull (MTEW) distributions based on generalized order statistics (GOS). We consider one-sample and two-sample prediction schemes using the Markov chain Monte Carlo (MCMC) algorithm. The conjugate prior is used to carry out the Bayesian analysis. The results are specialized to upper record values.

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“…For more details, see AL-Hussaini and Ahmad (2003), Ali Mousa (2003 and AL-Hussaini (2004). Recently, a few of authors utilized the GOS's in Bayesian inference.…”

Section: −T α and H(·) = F (·)mentioning

confidence: 99%

Section: −T α and H(·) = F (·)mentioning

confidence: 99%

Bayesian Prediction Based on Generalized Order Statistics from a Mixture of Two Exponentiated Weibull Distribution Via MCMC Sumulation

Abushal¹,

AL-Zaydi²

2012

IJSP

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“…Several authors utilized the concept of gos in their studies. References may be made to Kamps and Gather [2], Keseling [3], Cramer and Kamps [4], Ahsanullah [5], Habibullah and Ahsanullah [6], Raqab [7], Kamps and Cramer [8], Ahmad and Fawzy [9], Beiniek and Syznal [10], Al-Hussaini and Ahmad [11] , Cramer et al [12], Khan and Alzaid [13], Jaheen [14], Khan et al ([15], [16]), Khan and Zia [17] among others.…”

Section: Introductionmentioning

confidence: 99%

Moment Properties of Generalized Order Statistics from Exponential-Weibull Lifetime Distribution

Khan¹,

Khan²

2016

JAS

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Abstract. In this paper, simple explicit expressions and some recurrence relations satisfied by single and product moments of generalized order statistics from exponential-Weibull lifetime distribution have been obtained. These relations are deduced for moments of order statistics and upper record values. Further, conditional moments and a recurrence relation for single moments of the generalized order statistics are used to characterize this distribution and some computational works are also carried out.

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“…For various distributional properties of GOS , see Kamps (1995). The GOS have been considered extensively by many authors, among others, they are Ahsanullah (1996;2000), Kamps and Gather (1997), Cramer and Kamps (2000), Habibullah and Ahsanullah (2000), Jaheen (2002;2005a), AL-Hussaini and Ahmad (2003), AL-Hussaini (2004), Ahmad (2007;2008), Aboeleneen (2010), Jaheen and Al Harbi (2010), Abu El Fotouh (2011) and Ateya and Ahmad (2011).…”

Section: Introductionmentioning

confidence: 99%

Estimation Based on Generalized Order Statistics From a Mixture of Two Rayleigh Distributions

Abushal¹,

AL-Zaydi²

2012

IJSP

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This article is concerned with the problem of estimating the parameters, reliability and hazard rate functions of the mixture of two Rayleigh distributions (MT RD) based on generalized order statistics (GOS ). The maximum likelihood and Bayes methods of estimation are used for this purpose. The Markov chain Monte Carlo (MCMC) method is used for obtaining the Bayes estimates under the squared error loss and LINEX loss functions. Our results are specialized to progressive Type-II censored order statistics and upper record values. Comparisons are made between Bayesian and maximum likelihood estimators via a Monte Carlo simulation study.

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On Bayesian predictive distributions of generalized order statistics

Cited by 60 publications

References 29 publications

Bayesian Prediction Based on Generalized Order Statistics from a Mixture of Two Exponentiated Weibull Distribution Via MCMC Sumulation

Bayesian Prediction Based on Generalized Order Statistics from a Mixture of Two Exponentiated Weibull Distribution Via MCMC Sumulation

Moment Properties of Generalized Order Statistics from Exponential-Weibull Lifetime Distribution

Estimation Based on Generalized Order Statistics From a Mixture of Two Rayleigh Distributions

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