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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/12479
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dc.contributor.advisorBalakrishnan, Narayanaswamyen_US
dc.contributor.authorAlzahrani, Alyaen_US
dc.date.accessioned2014-06-18T16:59:47Z-
dc.date.available2014-06-18T16:59:47Z-
dc.date.created2012-09-14en_US
dc.date.issued2012-10en_US
dc.identifier.otheropendissertations/7363en_US
dc.identifier.other8416en_US
dc.identifier.other3322246en_US
dc.identifier.urihttp://hdl.handle.net/11375/12479-
dc.description.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>en_US
dc.subjectLog-logistic distributionen_US
dc.subjectProgressive Type-II right censoringen_US
dc.subjectMaximum likelihood estimatesen_US
dc.titleLIKELIHOOD INFERENCE FOR LOG-LOGISTIC DISTRIBUTION UNDER PROGRESSIVE TYPE-II RIGHT CENSORINGen_US
dc.typethesisen_US
dc.contributor.departmentMathematics and Statisticsen_US
dc.description.degreeMaster of Science (MSc)en_US
Appears in Collections:Open Access Dissertations and Theses

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