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The exponentiated-log-logistic geometric distribution: Dual activation

Author

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  • Natalie V. R. Mendoza
  • Edwin M. M. Ortega
  • Gauss M. Cordeiro

Abstract

The log-logistic distribution is commonly used to model lifetime data. We propose a wider distribution, named the exponentiated log-logistic geometric distribution, based on a double activation approach. We obtain the quantile function, ordinary moments, and generating function. The method of maximum likelihood is used to estimate the model parameters. We propose a new extended regression model based on the logarithm of the exponentiated log-logistic geometric distribution. This regression model can be very useful in the analysis of real data and could provide better fits than other special regression models. The potentiality of the new models is illustrated by means of two applications to real lifetime data sets.

Suggested Citation

  • Natalie V. R. Mendoza & Edwin M. M. Ortega & Gauss M. Cordeiro, 2016. "The exponentiated-log-logistic geometric distribution: Dual activation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(13), pages 3838-3859, July.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:13:p:3838-3859
    DOI: 10.1080/03610926.2014.909937
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    Cited by:

    1. Abdisalam Hassan Muse & Samuel Mwalili & Oscar Ngesa & Christophe Chesneau & Afrah Al-Bossly & Mahmoud El-Morshedy, 2022. "Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data," Mathematics, MDPI, vol. 10(20), pages 1-41, October.
    2. Abdisalam Hassan Muse & Samuel M. Mwalili & Oscar Ngesa, 2021. "On the Log-Logistic Distribution and Its Generalizations: A Survey," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(3), pages 1-93, June.
    3. Muhammad H Tahir & Gauss M. Cordeiro, 2016. "Compounding of distributions: a survey and new generalized classes," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-35, December.

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