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

Autor:	Jorge Figueroa-Zúñiga, Juan G. Toledo, Bernardo Lagos-Alvarez, Víctor Leiva, Jean P. Navarrete
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	EM and SEM algorithms Kumaraswamy distribution Metropolis–Hastings algorithm mixture models R software Mathematics QA1-939
Zdroj:	Mathematics, Vol 11, Iss 13, p 2894 (2023)
Druh dokumentu:	article
ISSN:	2227-7390
DOI:	10.3390/math11132894
Popis:	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.
Databáze:	Directory of Open Access Journals
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