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

Elisângela C. Biazatti, Gauss M. Cordeiro (), Gabriela M. Rodrigues, Edwin M. M. Ortega and Luís H. de Santana
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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

Stats, 2022, vol. 5, issue 4, 1-15

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.

Keywords: beta prime; censored data; COVID-19; inverted beta distribution; regression model; Weibull-G family (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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