Bayesian Prediction of the Median of the Burr Distribution with Fixed and Random Sample Sizes

Ashour, Samir K.; El-Wakeel, M.A.M.H.

doi:10.1080/02331889408802438

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where Bðu, vÞ is the beta function. For π i ðc, kÞ, i = 1, 2, 3, the E-Bayesian estimate of Y s with squared error loss function is obtained from (21) and (26) aŝ…”

Section: The E-bayesian Predictor Of Y S With Squared Error Lossmentioning

confidence: 99%

“…Prediction has been applied in medicine, engineering, business, and other areas as well. Many authors discussed prediction problems for many distributions, references to research done and review papers on prediction, in nonparametric and parametric settings, such as Al-Hussaini and Ahmad [19,20], Al-Hussaini and Jaheen [8,9], Ashour and El-Wakeel [21], Dunsmore [22], Guilbaud [23], Johnson et al [24], Nigm et al [25,26], Patel [27], Sindhu et al [28], and Singh et al [5]. For more details, one can refer to Aitchison and Dunsmore [29] and Geisser [30].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

E-Bayesian Prediction for the Burr XII Model Based on Type-II Censored Data with Two Samples

Okasha

Wang

2020

Advances in Mathematical Physics

View full text Add to dashboard Cite

Type-II censored data is an important scheme of data in lifetime studies. The purpose of this paper is to obtain E-Bayesian predictive functions which are based on observed order statistics with two samples from two parameter Burr XII model. Predictive functions are developed to derive both point prediction and interval prediction based on type-II censored data, where the median Bayesian estimation is a novel formulation to get Bayesian sample prediction, as the integral for calculating the Bayesian prediction directly does not exist. All kinds of predictions are obtained with symmetric and asymmetric loss functions. Two sample techniques are considered, and gamma conjugate prior density is assumed. Illustrative examples are provided for all the scenarios considered in this article. Both illustrative examples with real data and the Monte Carlo simulation are carried out to show the new method is acceptable. The results show that Bayesian and E-Bayesian predictions with the two kinds of loss functions have little difference for the point prediction, and E-Bayesian confidence interval (CI) with the two kinds of loss functions are almost similar and they are more accurate for the interval prediction.

show abstract

“…where Bðu, vÞ is the beta function. For π i ðc, kÞ, i = 1, 2, 3, the E-Bayesian estimate of Y s with squared error loss function is obtained from (21) and (26) aŝ…”

Section: The E-bayesian Predictor Of Y S With Squared Error Lossmentioning

confidence: 99%