Exponentiated Lomax Geometric Distribution: Properties and Applications

Amal Soliman Hassan, Marwa Abdallah Abdelghafar

Abstract


In this paper, a new four-parameter lifetime distribution, called the exponentiated Lomax geometric (ELG) is introduced. The new lifetime distribution contains the Lomax geometric and exponentiated Pareto geometric as new sub-models. Explicit algebraic formulas of probability density function, survival and hazard functions are derived. Various structural properties of the new model are derived including; quantile function, Re'nyi entropy, moments, probability weighted moments, order statistic, Lorenz and Bonferroni curves. The estimation of the model parameters is performed by maximum likelihood method and inference for a large sample is discussed. The flexibility and potentiality of the new model in comparison with some other distributions are shown via an application to a real data set. We hope that the new model will be an adequate model for applications in various studies.


Keywords


Exponentiated Lomax distribution, geometric distribution, Maximum likelihood estimation

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DOI: http://dx.doi.org/10.18187/pjsor.v13i3.1437

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Title

Exponentiated Lomax Geometric Distribution: Properties and Applications

Keywords

Exponentiated Lomax distribution, geometric distribution, Maximum likelihood estimation

Description

In this paper, a new four-parameter lifetime distribution, called the exponentiated Lomax geometric (ELG) is introduced. The new lifetime distribution contains the Lomax geometric and exponentiated Pareto geometric as new sub-models. Explicit algebraic formulas of probability density function, survival and hazard functions are derived. Various structural properties of the new model are derived including; quantile function, Re'nyi entropy, moments, probability weighted moments, order statistic, Lorenz and Bonferroni curves. The estimation of the model parameters is performed by maximum likelihood method and inference for a large sample is discussed. The flexibility and potentiality of the new model in comparison with some other distributions are shown via an application to a real data set. We hope that the new model will be an adequate model for applications in various studies.


Date

2017-09-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 13 No. 3, 2017



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