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A Weibull-Beta Prime Distribution to Model COVID-19 Data with the Presence of Covariates and Censored Data

Author

Listed:
  • Elisângela C. Biazatti

    (Department of Mathematics and Statistics, Federal University of Rondônia, Ji-Paraná 76900, Brazil)

  • Gauss M. Cordeiro

    (Departamento de Estatística, Universidade Federal de Pernambuco, Cidade Universitária, Recife 50670, Brazil)

  • Gabriela M. Rodrigues

    (Departamento de Ciências Exatas, Universidade de São Paulo, ESALQ/USP, Piracicaba 13418, Brazil)

  • Edwin M. M. Ortega

    (Departamento de Ciências Exatas, Universidade de São Paulo, ESALQ/USP, Piracicaba 13418, Brazil)

  • Luís H. de Santana

    (Departamento de Tecnologia, Universidade Estadual de Maringá, Umuarama 87506, Brazil)

Abstract

Motivated by the recent popularization of the beta prime distribution, a more flexible generalization is presented to fit symmetrical or asymmetrical and bimodal data, and a non-monotonic failure rate. Thus, the Weibull-beta prime distribution is defined, and some of its structural properties are obtained. The parameters are estimated by maximum likelihood, and a new regression model is proposed. Some simulations reveal that the estimators are consistent, and applications to censored COVID-19 data show the adequacy of the models.

Suggested Citation

  • Elisângela C. Biazatti & Gauss M. Cordeiro & Gabriela M. Rodrigues & Edwin M. M. Ortega & Luís H. de Santana, 2022. "A Weibull-Beta Prime Distribution to Model COVID-19 Data with the Presence of Covariates and Censored Data," Stats, MDPI, vol. 5(4), pages 1-15, November.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:4:p:69-1173:d:975279
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    References listed on IDEAS

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