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The Generalized Alpha Exponent Power Family of Distributions: Properties and Applications

Author

Listed:
  • Sajid Hussain

    (Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Muhammad Sajid Rashid

    (Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Mahmood Ul Hassan

    (Department of Statistics, Stockholm University, SE-106 91 Stockholm, Sweden)

  • Rashid Ahmed

    (Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

Abstract

Here, a new method is recommended to characterize a new continuous distribution class, named the generalized alpha exponent power family of distributions (GAEPFDs). A particular sub-model is presented for exemplifying the objective. The basic statistical properties, such as ordinary moments, the probability weighted moments, mode, quantile, order statistics, entropy measures, and moment generating functions, etc., were explored. To gauge the GAEPPRD parameters, the ML technique was utilized. The estimator behaviour was studied by a Monte Carlo simulation study (MCSS). The effectiveness of GAEPFDs was demonstrated observationally through lifetime data. The applications show that GAEPFDs can offer preferable results over other competitive models.

Suggested Citation

  • Sajid Hussain & Muhammad Sajid Rashid & Mahmood Ul Hassan & Rashid Ahmed, 2022. "The Generalized Alpha Exponent Power Family of Distributions: Properties and Applications," Mathematics, MDPI, vol. 10(9), pages 1-19, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1421-:d:800356
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    References listed on IDEAS

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    2. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    3. Muhammad Ali & Alamgir Khalil & Muhammad Ijaz & Noor Saeed, 2021. "Alpha-Power Exponentiated Inverse Rayleigh distribution and its applications to real and simulated data," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-17, January.
    4. Ayman Alzaatreh & Carl Lee & Felix Famoye, 2013. "A new method for generating families of continuous distributions," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 63-79, June.
    5. 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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    Cited by:

    1. Ahmad Abubakar Suleiman & Hanita Daud & Narinderjit Singh Sawaran Singh & Mahmod Othman & Aliyu Ismail Ishaq & Rajalingam Sokkalingam, 2023. "A Novel Odd Beta Prime-Logistic Distribution: Desirable Mathematical Properties and Applications to Engineering and Environmental Data," Sustainability, MDPI, vol. 15(13), pages 1-25, June.
    2. Sajid Hussain & Muhammad Sajid Rashid & Mahmood Ul Hassan & Rashid Ahmed, 2022. "The Generalized Exponential Extended Exponentiated Family of Distributions: Theory, Properties, and Applications," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    3. Sajid Hussain & Mahmood Ul Hassan & Muhammad Sajid Rashid & Rashid Ahmed, 2023. "The Exponentiated Power Alpha Index Generalized Family of Distributions: Properties and Applications," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    4. Sajid Hussain & Mahmood Ul Hassan & Muhammad Sajid Rashid & Rashid Ahmed, 2023. "Families of Extended Exponentiated Generalized Distributions and Applications of Medical Data Using Burr III Extended Exponentiated Weibull Distribution," Mathematics, MDPI, vol. 11(14), pages 1-24, July.

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