Title: A computational method for estimating Burr XII parameters with complete and multiple censored data,
Abstract: Flexibility in shape and scale of Burr XII distribution can make close
approximation of numerous well-known probability density functions. Due to
these capabilities, the usages of Burr XII distribution are applied in risk
analysis, lifetime data analysis and process capability estimation. In this
paper the Cross-Entropy (CE) method is further developed in terms of Maximum
Likelihood Estimation (MLE) to estimate the parameters of Burr XII distribution
for the complete data or in the presence of multiple censoring. A simulation
study is conducted to evaluate the performance of the MLE by means of CE method
for different parameter settings and sample sizes. The results are compared to
other existing methods in both uncensored and censored situations.