On Estimation and Prediction for the Inverted Kumaraswamy Distribution Based on General Progressive Censored Samples

In this article, the problem of estimating unknown parameters of the inverted kumaraswamy (IKum) distribution is considered based on general progressive Type-II censored Data. The maximum likelihood (MLE) estimators of the parameters are obtained while the Bayesian estimates are obtained using the s...

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Bibliographic Details
Published in:Pakistan journal of statistics and operation research 2018-01, Vol.14 (3), p.717
Main Authors: Abu-Moussa, Mahmoud H., Mohie El-Din, Mostafa M.
Format: Article
Language:English
Subjects:
Bayesian analysis
Confidence intervals
Maximum likelihood estimators
Parameter estimation
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Summary:In this article, the problem of estimating unknown parameters of the inverted kumaraswamy (IKum) distribution is considered based on general progressive Type-II censored Data. The maximum likelihood (MLE) estimators of the parameters are obtained while the Bayesian estimates are obtained using the squared error loss(SEL) as symmetric loss function. Also we used asymmetric loss functions as the linear-exponential loss (LINEX), generalized entropy (GE) and Al-Bayyati loss function (AL-Bayyati). Lindely's approximation method is used to evaluate the Bayes estimates. We also derived an approximate confidence interval for the parameters of the inverted Kumaraswamy distribution. Two-sample Bayesian prediction intervals are constructed with an illustrative example. Finally, simulation study concerning different sample sizes and different censoring schemes were reported.
ISSN:1816-2711
2220-5810
DOI:10.18187/pjsor.v14i3.2103