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|Title:||LIKELIHOOD INFERENCE FOR LOG-LOGISTIC DISTRIBUTION UNDER PROGRESSIVE TYPE-II RIGHT CENSORING|
|Department:||Mathematics and Statistics|
|Keywords:||Log-logistic distribution;Progressive Type-II right censoring;Maximum likelihood estimates|
|Abstract:||<p>Censoring arises quite often in lifetime data. Its presence may be planned or unplanned. In this project, we demonstrate progressive Type-II right censoring when the underlying distribution is log-logistic. The objective is to discuss inferential methods for the unknown parameters of the distribution based on the maximum likelihood estimation method. The Newton-Raphson method is proposed as a numerical technique to solve the pertinent non-linear equations. In addition, confidence intervals for the unknown parameters are constructed based on (i) asymptotic normality of the maximum likelihood estimates, and (ii) percentile bootstrap resampling technique. A Monte Carlo simulation study is conducted to evaluate the performance of the methods of inference developed here. Some illustrative examples are also presented.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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