TITLE:
Bayesian and Frequentist Prediction Using Progressive Type-II Censored with Binomial Removals
AUTHORS:
Ahmed A. Soliman, Ahmed H. Abd Ellah, Nasser A. Abou-Elheggag, Rashad M. El-Sagheer
KEYWORDS:
Bayesian Prediction; Burr-X Model; Progressive Censoring; Random Removals
JOURNAL NAME:
Intelligent Information Management,
Vol.5 No.5,
September
24,
2013
ABSTRACT: In this article, we study the problem of predicting future records and order statistics (two-sample prediction) based on progressive type-II censored with random removals, where the number of units removed at each failure time has a discrete binomial distribution. We use the Bayes procedure to derive both point and interval bounds prediction. Bayesian point prediction under symmetric and symmetric loss functions is discussed. The maximum likelihood (ML) prediction intervals using “plug-in” procedure for future records and order statistics are derived. An example is discussed to illustrate the application of the results under this censoring scheme.