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; Jean P. Navarrete

doi:10.3390/math11132894

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, Jean P. Navarrete

SCHOOL OF INDUSTRIAL ENGINEERING

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2 Scopus citations

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.

Original language	English
Article number	2894
Journal	Mathematics
Volume	11
Issue number	13
DOIs	https://doi.org/10.3390/math11132894
State	Published - Jul 2023

Keywords

EM and SEM algorithms
Kumaraswamy distribution
Metropolis–Hastings algorithm
R software
mixture models

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10.3390/math11132894

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

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, R software, mixture models",

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AU - Lagos-Alvarez, Bernardo

AU - Leiva, Víctor

AU - Navarrete, Jean P.

PY - 2023/7

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N2 - 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.

AB - 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.

KW - EM and SEM algorithms

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KW - Metropolis–Hastings algorithm

KW - R software

KW - mixture models

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

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Keywords

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