Article Sidebar

Download
PDF
Statistic
Read Counter : 315 Download : 459

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Main Article Content

Abstract

In this paper, we introduce the exponentiated generalized alpha power family of distributions to extend the several other distributions. We used the new family to discuss the exponentiated generalized alpha power exponential (EGAPEx) distribution. Some statistical properties of the EGAPEx distribution are obtained. The model parameters are obtained by the maximum likelihood estimation (MLE), maximum product spacing (MPS) and Bayesian estimation methods. A Monte Carlo Simulation is performed to compare between different methods. We illustrate the performance of the proposed new family of distributions by means of two real data sets and the data sets show the new family of distributions is more appropriate as compared to the exponentiated generalized exponential, alpha power generalized exponential, alpha power exponential, generalized exponential and exponential distributions.

Keywords

Exponentiated Generalized Alpha Power Maximum Likelihood Estimation Maximum Product Spacing Bayesian estimation Reliability Analysis

Article Details

Creative Commons License

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

Authors who publish with this journal agree to the following License

CC BY: This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

 

How to Cite
ElSherpieny, E. A., & Almetwally, E. M. (2022). The Exponentiated Generalized Alpha Power Family of Distribution: Properties and Applications. Pakistan Journal of Statistics and Operation Research, 18(2), 349-367. https://doi.org/10.18187/pjsor.v18i2.3515
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Almetwally, E. M., & Almongy, H. M. (2019a). Estimation Methods for the New Weibull-Pareto Distribution: Simulation and Application. Journal of Data Science, 17(3), 610-630.†DOI: https://doi.org/10.6339/JDS.201907_17(3).0009
  2. Almetwally, E. M., & Almongy, H. M. (2019b). Maximum Product Spacing and Bayesian Method for Parameter Estimation for Generalized Power Weibull Distribution under Censoring Scheme. Journal of Data Science, 17(2), 407-444.†DOI: https://doi.org/10.6339/JDS.201904_17(2).0010
  3. Almetwally, E. M., Almongy, H. M., & El sayed Mubarak, A. (2018). Bayesian and Maximum Likelihood Estimation for the Weibull Generalized Exponential Distribution Parameters Using Progressive Censoring Schemes. Pakistan Journal of Statistics and Operation Research, 14(4), 853-868.†DOI: https://doi.org/10.18187/pjsor.v14i4.2600
  4. Almetwally, E. M., Almongy, H. M., & El-Sherpieny, E. A. (2019). Adaptive Type-II Progressive Censoring Schemes based on Maximum Product Spacing with Application of Generalized Rayleigh Distribution. Journal of Data Science, 17(4), 767 - 793. DOI: https://doi.org/10.6339/JDS.201910_17(4).0010
  5. Andrade, T., Rodrigues, H., Bourguignon, M., & Cordeiro, G. (2015). The exponentiated generalized Gumbel distribution. Revista Colombiana de Estadística, 38(1), 123-143.†DOI: https://doi.org/10.15446/rce.v38n1.48806
  6. Aryal, G., & Elbatal, I. (2015). On the exponentiated generalized modified Weibull distribution. Communications for Statistical Applications and Methods, 22(4), 333-348.†DOI: https://doi.org/10.5351/CSAM.2015.22.4.333
  7. Bader, M. G., & Priest, A. M. (1982). Statistical aspects of fibre and bundle strength in hybrid composites. Progress in science and engineering of composites, 1129-1136.â€
  8. Basheer, A. M. (2019). Alpha power inverse Weibull distribution with reliability application. Journal of Taibah University for Science, 13(1), 423-432.†DOI: https://doi.org/10.1080/16583655.2019.1588488
  9. Cordeiro, G. M., & Lemonte, A. J. (2014). The exponentiated generalized Birnbaum-Saunders distribution. Applied Mathematics and Computation, 247, 762-779.†DOI: https://doi.org/10.1016/j.amc.2014.09.054
  10. Cordeiro, G. M., Ortega, E. M., & da Cunha, D. C. (2013). The exponentiated generalized class of distributions. Journal of Data Science, 11(1), 1-27.†DOI: https://doi.org/10.6339/JDS.201301_11(1).0001
  11. De Andrade, T. A., Bourguignon, M., & Cordeiro, G. M. (2016). The exponentiated generalized extended exponential distribution. Journal of Data Science, 14(3), 393-413.†DOI: https://doi.org/10.6339/JDS.201607_14(3).0001
  12. Dey, S., Ghosh, I., & Kumar, D. (2018). Alpha-power transformed lindley distribution: properties and associated inference with application to earthquake data. Annals of Data Science, 1-28. DOI: https://doi.org/10.1007/s40745-018-0163-2
  13. Dey, S., Nassar, M., & Kumar, D. (2019). Alpha power transformed inverse Lindley distribution: A distribution with an upside-down bathtub-shaped hazard function. Journal of Computational and Applied Mathematics, 348, 130-145.††DOI: https://doi.org/10.1016/j.cam.2018.03.037
  14. Elbatal, I., Ahmad, Z., Elgarhy, B. M., & Almarashi, A. M. (2018). A New Alpha Power Transformed Family of Distributions: Properties and Applications to the Weibull Model. Journal of Nonlinear Science and Applications, 12(1), 1-20.†DOI: https://doi.org/10.22436/jnsa.012.01.01
  15. Elgarhy, M., ul Haq, M. A., & ul Ain, Q. (2018). Exponentiated Generalized Kumaraswamy Distribution with Applications. Annals of Data Science, 5(2), 273-292.†DOI: https://doi.org/10.1007/s40745-017-0128-x
  16. Handique, L., Chakraborty, S., & de Andrade, T. A. (2018). The Exponentiated Generalized Marshall-Olkin Family of Distribution: Its Properties and Applications. Annals of Data Science, 1-21.†DOI: https://doi.org/10.1007/s40745-018-0166-z
  17. Hassan, A. S., & Abd-Allah, M. (2019). On the Inverse Power Lomax Distribution. Annals of Data Science, 6(2), 259-278.†DOI: https://doi.org/10.1007/s40745-018-0183-y
  18. Mahdavi, A., & Kundu, D. (2017). A new method for generating distributions with an application to exponential distribution. Communications in Statistics-Theory and Methods, 46(13), 6543-6557.†DOI: https://doi.org/10.1080/03610926.2015.1130839
  19. Nasiru, S., Mwita, P. N., & Ngesa, O. (2018). Exponentiated generalized power series family of distributions. Annals of Data Science, 1-27.†DOI: https://doi.org/10.1007/s40745-018-0170-3
  20. Nassar, M., Alzaatreh, A., Mead, M., & Abo-Kasem, O. (2017). Alpha power Weibull distribution: Properties and applications. Communications in Statistics-Theory and Methods, 46(20), 10236-10252.†DOI: https://doi.org/10.1080/03610926.2016.1231816
  21. Reyad, H. M., Alizadeh, M., Jamal, F., Othman, S., & Hamedani, G. G. (2019). The Exponentiated Generalized Topp Leone-G Family of Distributions: Properties and Applications. Pakistan Journal of Statistics and Operation Research, 15(1), 1-24.†DOI: https://doi.org/10.18187/pjsor.v15i1.2166
  22. Smith, R. L., & Naylor, J. (1987). A comparison of maximum likelihood and Bayesian estimators for the threeâ€parameter Weibull distribution. Journal of the Royal Statistical Society: Series C (Applied Statistics), 36(3), 358-369.†DOI: https://doi.org/10.2307/2347795