Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile

Jorge Figueroa-Zúñiga (), Juan G. Toledo, Bernardo Lagos-Alvarez, Víctor Leiva () and Jean P. Navarrete
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Jorge Figueroa-Zúñiga: Departamento de Estadística, Universidad de Concepción, Concepción 4070386, Chile
Juan G. Toledo: Departamento de Estadística, Universidad de Concepción, Concepción 4070386, Chile
Bernardo Lagos-Alvarez: Departamento de Estadística, Universidad de Concepción, Concepción 4070386, Chile
Víctor Leiva: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Jean P. Navarrete: Departamento de Matemática, Universidad del Bío-Bío, Concepción 4051381, Chile

Mathematics, 2023, vol. 11, issue 13, 1-14

Abstract: Extensive research has been conducted on models that utilize the Kumaraswamy distribution to describe continuous variables with bounded support. In this study, we examine the trapezoidal Kumaraswamy model. Our objective is to propose a parameter estimation method for this model using the stochastic expectation maximization algorithm, which effectively tackles the challenges commonly encountered in the traditional expectation maximization algorithm. We then apply our results to the modeling of daily COVID-19 cases in Chile.

Keywords: EM and SEM algorithms; Kumaraswamy distribution; Metropolis–Hastings algorithm; mixture models; R software (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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