LIKELIHOOD INFERENCE FOR LOG-LOGISTIC DISTRIBUTION UNDER PROGRESSIVE TYPE-II RIGHT CENSORING
Date of Award
Master of Science (MSc)
Mathematics and Statistics
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.
Alzahrani, Alya, "LIKELIHOOD INFERENCE FOR LOG-LOGISTIC DISTRIBUTION UNDER PROGRESSIVE TYPE-II RIGHT CENSORING" (2012). Open Access Dissertations and Theses. Paper 7363.
McMaster University Library
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