Title

LIKELIHOOD INFERENCE FOR LOG-LOGISTIC DISTRIBUTION UNDER PROGRESSIVE TYPE-II RIGHT CENSORING

Date of Award

Fall 2012

Degree Type

Thesis

Degree Name

Master of Science (MSc)

Department

Mathematics and Statistics

Supervisor

N. Balakrishnan

Language

English

Abstract

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.

McMaster University Library



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