A General Transmuted Family of Distributions

Md. Mahabubur Rahman, Bander Al-Zahrani, Muhammad Qaiser Shahbaz

Abstract


In this article we have proposed a general transmuted family of distributions with emphasis on the cubic transmuted family of distributions. This new class of distributions provide additional exibility in modeling the bi-modal data. The proposed cubic transmuted family of distributions has been linked with the T-X family of distributions proposed by Alzaatreh et al. (2013). Some members of the proposed family of distributions have been discussed. The cubic transmuted exponential distribution has been discussed in detail and various statistical properties of the distribution have been explored. The maximum likelihood estimation for parameters of cubic transmuted exponential distribution has also been discussed alongside Monte Carlo simulation study to assess the performance of the estimation procedure. Finally, the cubic transmuted exponential distribution has been tted to real datasets to investigate it's applicability.

Keywords


Cubic transmuted distribution, Exponential distribution, General transmutation, Maximum likelihood estimation, Reliability analysis

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DOI: http://dx.doi.org/10.18187/pjsor.v14i2.2334

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Title

A General Transmuted Family of Distributions

Keywords

Cubic transmuted distribution, Exponential distribution, General transmutation, Maximum likelihood estimation, Reliability analysis

Description

In this article we have proposed a general transmuted family of distributions with emphasis on the cubic transmuted family of distributions. This new class of distributions provide additional exibility in modeling the bi-modal data. The proposed cubic transmuted family of distributions has been linked with the T-X family of distributions proposed by Alzaatreh et al. (2013). Some members of the proposed family of distributions have been discussed. The cubic transmuted exponential distribution has been discussed in detail and various statistical properties of the distribution have been explored. The maximum likelihood estimation for parameters of cubic transmuted exponential distribution has also been discussed alongside Monte Carlo simulation study to assess the performance of the estimation procedure. Finally, the cubic transmuted exponential distribution has been tted to real datasets to investigate it's applicability.

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