On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring

Elsayed Ahmed Elsherpieny, Hiba Zeyada Muhammed, Noha Usama Mohamed Mohamed Radwan

Abstract


Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to discriminate between them. Some simulation experiments were performed to see how the probability of correct selection (PCS) under each model work for small sample sizes. Real data life is analyzed to see how the proposed method works in practice. As a special case of progressive type II censoring, the problem of discriminating between gamma and log-logistic in case of complete samples is considered. The RML and the ratio of Minimized Kullback-Leibler Divergence (RMKLD) tests are used to discriminate between them. The asymptotic results are used to estimate the PCS which is used to calculate the minimum sample size required for discriminating between two distributions. Two real life data are analyzed.

Keywords


Keywords: Gamma distribution; Log-logistic distribution; Progressive type II censoring, Likelihood ratio statistic; the ratio of Minimized Kullback-Leibler Divergence.

Full Text:

PDF


DOI: http://dx.doi.org/10.18187/pjsor.v13i1.1524

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 Pakistan Journal of Statistics and Operation Research

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Title

On Discriminating between Gamma and Log-logistic Distributions in Case of Progressive Type II Censoring

Keywords

Keywords: Gamma distribution; Log-logistic distribution; Progressive type II censoring, Likelihood ratio statistic; the ratio of Minimized Kullback-Leibler Divergence.

Description

Gamma and log-logistic distributions are two popular distributions for analyzing lifetime data. In this paper, the problem of discriminating between these two distribution functions is considered in case of progressive type II censoring. The ratio of the maximized likelihood test (RML) is used to discriminate between them. Some simulation experiments were performed to see how the probability of correct selection (PCS) under each model work for small sample sizes. Real data life is analyzed to see how the proposed method works in practice. As a special case of progressive type II censoring, the problem of discriminating between gamma and log-logistic in case of complete samples is considered. The RML and the ratio of Minimized Kullback-Leibler Divergence (RMKLD) tests are used to discriminate between them. The asymptotic results are used to estimate the PCS which is used to calculate the minimum sample size required for discriminating between two distributions. Two real life data are analyzed.

Date

2017-03-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 13 No. 1, 2017



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