TITLE:
Estimation Based on Progressive First-Failure Censored Sampling with Binomial Removals
AUTHORS:
Ahmed A. Soliman, Ahmed H. Abd Ellah, Nasser A. Abou-Elheggag, Rashad M. El-Sagheer
KEYWORDS:
Burr-X Distribution; Progressive First-Failure Censored; Bayesian and Non-Bayesian Estimations; Loss Function; Bootstrap; Random Removals
JOURNAL NAME:
Intelligent Information Management,
Vol.5 No.4,
July
17,
2013
ABSTRACT: In this paper, the inference for the Burr-X model under
progressively first-failure censoring scheme is discussed. Based on this new
censoring were the number of units removed at each failure time has a discrete
binomial distribution. The maximum likelihood, Bootstrap and Bayes estimates
for the Burr-X distribution are obtained. The Bayes estimators are obtained
using both the symmetric and asymmetric loss functions. Approximate confidence
interval and highest posterior density interval (HPDI) are discussed. A
numerical example is provided to illustrate the proposed estimation methods
developed here. The maximum likelihood and the different Bayes estimates are
compared via a Monte Carlo simulation study.