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Estimation Using Censored Data from Exponentiated Burr Type XII Population

Author(s): Essam K. AL-Hussaini | Mohamed Hussein

Journal: Open Journal of Statistics
ISSN 2161-718X

Volume: 01;
Issue: 02;
Start page: 33;
Date: 2011;
Original page

Keywords: Exponentiated Distribution | Proportional Reversed Hazard Rate Model | Lehmann Alternatives | Maximum Likelihood and Bayes Estimation | Burr Type XII Distribution | Subjective Prior | SE and LINEX Loss Functions | MCMC

Maximum likelihood and Bayes estimators of the parameters, survival function (SF) and hazard rate function (HRF) are obtained for the three-parameter exponentiated Burr type XII distribution when sample is available from type II censored scheme. Bayes estimators have been developed using the standard Bayes and MCMC methods under square error and LINEX loss functions, using informative type of priors for the parameters. Simulation comparison of various estimation methods is made when n = 20, 40, 60 and censored data. The Bayes estimates are found to be, generally, better than the maximum likelihood estimates against the proposed prior, in the sense of having smaller mean square errors. This is found to be true whether the data are complete or censored. Estimates improve by increasing sample size. Analysis is also carried out for real life data.
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