On Maximum Likelihood Estimation for Left Censored Burr Type III Distribution

Navid Feroze, Muhammad Aslam, Tabassum Naz Sindhu

Abstract


Burr type III is an important distribution used to model the failure time data. The paper addresses the problem of estimation of parameters of the Burr type III distribution based on maximum likelihood estimation (MLE) when the samples are left censored. As the closed form expression for the MLEs of the parameters cannot be derived, the approximate solutions have been obtained through iterative procedures. An extensive simulation study has been carried out to investigate the performance of the estimators with respect to sample size, censoring rate and true parametric values. A real life example has also been presented. The study revealed that the proposed estimators are consistent and capable of providing efficient results under small to moderate samples.


Keywords


Maximum likelihood estimation, loss functions, prior distribution, Bayes risks

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DOI: http://dx.doi.org/10.18187/pjsor.v11i4.648

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Title

On Maximum Likelihood Estimation for Left Censored Burr Type III Distribution

Keywords

Maximum likelihood estimation, loss functions, prior distribution, Bayes risks

Description

Burr type III is an important distribution used to model the failure time data. The paper addresses the problem of estimation of parameters of the Burr type III distribution based on maximum likelihood estimation (MLE) when the samples are left censored. As the closed form expression for the MLEs of the parameters cannot be derived, the approximate solutions have been obtained through iterative procedures. An extensive simulation study has been carried out to investigate the performance of the estimators with respect to sample size, censoring rate and true parametric values. A real life example has also been presented. The study revealed that the proposed estimators are consistent and capable of providing efficient results under small to moderate samples.


Date

2015-12-03

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 11 No. 4, 2015



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810