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The Logit Exponentiated Power Exponential Regression with Applications

Author

Listed:
  • Fábio Prataviera

    (ESALQ/USP)

  • Aline Martineli Batista

    (ESALQ/USP)

  • Edwin M. M. Ortega

    (ESALQ/USP)

  • Gauss M. Cordeiro

    (UFPE)

  • Bruno Montoani Silva

    (UFLA)

Abstract

We introduce a new distribution, called the logit exponentiated power exponential, defined on the unit interval. Explicit expansions are derived for its moments. Also, we propose a regression based on this distribution with two systematic components, which can provide better fits than the beta and simplex regressions. Its parameters are estimated by maximum likelihood. Some simulations investigate the accuracy of the estimates. The usefulness of the new models is proved by means of three real data sets.

Suggested Citation

  • Fábio Prataviera & Aline Martineli Batista & Edwin M. M. Ortega & Gauss M. Cordeiro & Bruno Montoani Silva, 2023. "The Logit Exponentiated Power Exponential Regression with Applications," Annals of Data Science, Springer, vol. 10(3), pages 713-735, June.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:3:d:10.1007_s40745-021-00347-8
    DOI: 10.1007/s40745-021-00347-8
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    References listed on IDEAS

    as
    1. F. Prataviera & J. C. S. Vasconcelos & G. M. Cordeiro & E. M. Hashimoto & E. M. M. Ortega, 2019. "The exponentiated power exponential regression model with different regression structures: application in nursing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(10), pages 1792-1821, July.
    2. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    3. de Moura, Maíse Soares & Silva, Bruno Montoani & Mota, Paula Karen & Borghi, Emerson & Resende, Alvaro Vilela de & Acuña-Guzman, Salvador Francisco & Araújo, Gabriela Soares Santos & da Silva, Lucas d, 2021. "Soil management and diverse crop rotation can mitigate early-stage no-till compaction and improve least limiting water range in a Ferralsol," Agricultural Water Management, Elsevier, vol. 243(C).
    4. Emrah Altun, 2021. "The log-weighted exponential regression model: alternative to the beta regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(10), pages 2306-2321, May.
    5. Yuancheng Si, 2020. "Pivot Property in Weighted Least Regression Based on Single Repeated Observations," Annals of Data Science, Springer, vol. 7(2), pages 291-306, June.
    6. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    7. Chen, Dianyu & Hsu, Kuolin & Duan, Xingwu & Wang, Youke & Wei, Xinguang & Muhammad, Saifullah, 2020. "Bayesian analysis of jujube canopy transpiration models: Does embedding the key environmental factor in Jarvis canopy resistance sub-model always associate with improving transpiration modeling?," Agricultural Water Management, Elsevier, vol. 234(C).
    8. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
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