Prediction intervals for future records and order statistics coming from two parameter exponential distribution

Ahmadi, Jafar; MirMostafaee, S.M.T.K.

doi:10.1016/j.spl.2008.12.002

Cited by 27 publications

(12 citation statements)

References 18 publications

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“…Based on an informative progressively first failure censored sample, our aim is to predict the upper record values. The conditional pdf of th S s Z for given ,   is given see Ahmadi and MirMostafaee [20],…”

Section: Bayesian Prediction For Future Record Valuementioning

confidence: 99%

“…Ahmadi et al [19] considered the Bayesian prediction of order statistics based on k-record values from exponential distribution. Ahmadi and MirMostafaee [20] obtained prediction intervals for order statistics as well as for the mean life time from a future sample based on observed usual records from an exponential distribution. Ali Mousa and Al-Sagheer [21] discussed the prediction problems for the Rayleigh based on progressively Type-II censored data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling

Soliman¹,

Abdellah²,

Abou-Elheggag³

et al. 2011

IIM

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This paper deals with Bayesian inference and prediction problems of the Burr type XII distribution based on progressive first failure censored data. We consider the Bayesian inference under a squared error loss function. We propose to apply Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples, and they have in turn, been used to compute the Bayes estimates with the help of importance sampling technique. We have performed a simulation study in order to compare the proposed Bayes estimators with the maximum likelihood estimators. We further consider two sample Bayes prediction to predicting future order statistics and upper record values from Burr type XII distribution based on progressive first failure censored data. The predictive densities are obtained and used to determine prediction intervals for unobserved order statistics and upper record values. A real life data set is used to illustrate the results derived

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Section: Bayesian Prediction For Future Record Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling

Soliman¹,

Abdellah²,

Abou-Elheggag³

et al. 2011

IIM

View full text Add to dashboard Cite

show abstract

“…This type of prediction plan was discussed recently by Ahmadi and Balakrishnan [19], and they addressed the problem of predicting usual records (order statistics) from an independent future Y -sequence based on order statistics (usual records) observed from a current X-sample, and derived several nonparametric prediction intervals. In this regard, Ahmadi and MirMostafaee [20] and Ahmadi et al [21] obtained prediction intervals for order statistics as well as for the mean lifetime from a future sample based on observed usual records from an exponential distribution using the the frequentist and Bayesian approaches, respectively. It is well known that the Bayesian approach allows prior knowledge as well as experimental data to be incorporated into the inferential procedure, thus avoiding the difficulties of the frequentist approach in analysing censored data.…”

Section: Introductionmentioning

confidence: 99%

Bayesian prediction ofk-record values based on progressively censored data from exponential distribution

Ahmadi¹,

MirMostafaee²

2012

Journal of Statistical Computation and Simulation

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“…Recently, discussed how one can predict future usual records (order statistics) from an independent Y -sequence based on order statistics (usual records) from an independent X -sequence and developed nonparametric prediction intervals. Ahmadi and MirMostafaee (2009) and obtained prediction intervals for order statistics as well as for the mean life time from a future sample based on observed usual records from an exponential distribution using the classical and Bayesian approaches, respectively. Here, along the lines of , we consider the case of records and order statistics jointly and discuss the construction of prediction intervals for order statistics from a future independent Y -sample based on observed current records as well as record coverage from an independent X -sequence.…”

Section: Introductionmentioning

confidence: 99%

Distribution-free prediction intervals for order statistics based on record coverage

Ahmadi

2011

Journal of the Korean Statistical Society

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a b s t r a c tIn this paper, based on the largest and smallest observations at the times when a new record of either kind (upper or lower) occurs, we discuss the prediction of future order statistics. The proposed prediction intervals are distribution-free in that the corresponding coverage probabilities are known exactly without any assumption about the parent distribution other than that it being continuous. An exact expression for the prediction coefficient of these intervals is derived. Similarly, prediction intervals for future records based on observed order statistics are also obtained. Finally, two real-life data, one involving the average July temperatures in Neurenburg, Switzerland, and the other involving the amount of annual rainfall at the Los Angeles Civic Center, are used to illustrate the procedures developed here.

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Prediction intervals for future records and order statistics coming from two parameter exponential distribution

Cited by 27 publications

References 18 publications

Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling

Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling

Bayesian prediction ofk-record values based on progressively censored data from exponential distribution

Distribution-free prediction intervals for order statistics based on record coverage

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