Extended Half-Logistic Distribution with Theory and Lifetime Data Application

Emrah Altun, Muhammad Nauman Khan, Morad Alizadeh, Gamze Ozel, Nadeem Shafique Butt

Abstract


In this paper, we introduce a new three-parameter lifetime model called the extended half-logistic (EHL) distribution. We derive various of its structural properties including moments, quantile and generating functions, mixture representation for probability density function, and reliability curves. The maximum likelihood, ordinary and weighted least square methods are used to estimate the model parameters. Simulation results to assess the performance of the estimation methods are discussed. We conclude that the maximum likelihood is the most suitable method to estimate model parameters for the small sample size. While the weighted least square method is the best for the large sample size. Finally, we prove empirically the importance and flexibility of the new model in modeling a real lifetime dataset.


Keywords


Half-logistic distribution, Meijer’s G–functions, Weighted Least Square

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DOI: http://dx.doi.org/10.18187/pjsor.v14i2.2396

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Title

Extended Half-Logistic Distribution with Theory and Lifetime Data Application

Keywords

Half-logistic distribution, Meijer’s G–functions, Weighted Least Square

Description

In this paper, we introduce a new three-parameter lifetime model called the extended half-logistic (EHL) distribution. We derive various of its structural properties including moments, quantile and generating functions, mixture representation for probability density function, and reliability curves. The maximum likelihood, ordinary and weighted least square methods are used to estimate the model parameters. Simulation results to assess the performance of the estimation methods are discussed. We conclude that the maximum likelihood is the most suitable method to estimate model parameters for the small sample size. While the weighted least square method is the best for the large sample size. Finally, we prove empirically the importance and flexibility of the new model in modeling a real lifetime dataset.


Date

2018-06-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 14 No. 2, 2018



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