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.
Cite this paper
Soliman, A. , Ellah, A. , Abou-Elheggag, N. and El-Sagheer, R. (2013) Estimation Based on Progressive First-Failure Censored Sampling with Binomial Removals. Intelligent Information Management, 5, 117-125. doi: 10.4236/iim.2013.54012.
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