Best Prediction Method for Progressive Type-II Censored Samples under New Pareto Model with Applications

This paper describes two prediction methods for predicting the non-observed (censored) units under progressive Type-II censored samples. The lifetimes under consideration are following a new two-parameter Pareto distribution. Furthermore, point and interval estimation of the unknown parameters of th...

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Bibliographic Details
Published inJournal of mathematics (Hidawi) Vol. 2021; pp. 1 - 11
Main Author Haj Ahmad, Hanan
Format Journal Article
LanguageEnglish
Published Cairo Hindawi 2021
Hindawi Limited
Wiley
Subjects
Bayesian analysis
Estimators
Experiments
Markov chains
Mathematical models
Mathematics
Maximum likelihood estimation
Maximum likelihood method
Numerical analysis
Parameter estimation
Predictions
Random variables
Simulation
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Summary:This paper describes two prediction methods for predicting the non-observed (censored) units under progressive Type-II censored samples. The lifetimes under consideration are following a new two-parameter Pareto distribution. Furthermore, point and interval estimation of the unknown parameters of the new Pareto model is obtained. Maximum likelihood and Bayesian estimation methods are considered for that purpose. Since Bayes estimators cannot be expressed explicitly, Gibbs and the Markov Chain Monte Carlo techniques are utilized for Bayesian calculation. We use the posterior predictive density of the non-observed units to construct predictive intervals. A simulation study is performed to evaluate the performance of the estimators via mean square errors and biases and to obtain the best prediction method for the censored observation under progressive Type-II censoring scheme for different sample sizes and different censoring schemes.
ISSN:2314-4629
2314-4785
DOI:10.1155/2021/1355990