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

Figueroa-Zuniga, Jorge; Toledo, Juan G.; Lagos-Alvarez, Bernardo; Leiva, Victor; Navarrete, Jean P.

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.

Más información

Título según WOS: Inference Based on the Stochastic Expectation Maximization Algorithm in a Kumaraswamy Model with an Application to COVID-19 Cases in Chile
Título de la Revista: MATHEMATICS
Volumen: 11
Número: 13
Editorial: MDPI
Fecha de publicación: 2023
DOI:

10.3390/math11132894

Notas: ISI