Héctor Andrés Muñoz

Mr. Héctor Andrés Muñoz


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
18 January 2023 in Mathematics
Verified
Journal Article
Open access
PDF file
JCJaime S. Castillo
Jaime S. Castillo
KGKatherine P. Gaete
Katherine P. Gaete
HMHéctor Andrés Muñoz
Héctor Andrés Muñoz
Diego Ignacio GallardoDiego Ignacio Gallardo
Diego Ignacio Gallardo
Marcelo BourguignonMarcelo Bourguignon
Marcelo Bourguignon
Héctor GómezHéctor Gómez
Héctor Gómez
This paper presents a new distribution, the product of the mixture between Maxwell-Boltzmann and a particular case of the generalized gamma distributions. The resulting distribution, called the Scale Mixture Maxwell-Boltzmann, presents greater kurtosis than the recently introduced slash Maxwell-Boltzmann distribution. We obtained closed-form expressions for its probability density and cumulative distribution functions. We studied some of its properties and moments, as well as its skewness and kurtosis coefficients. Parameters were estimated by the moments and maximum likelihood methods, via the Expectation-Maximization algorithm for the latter case. A simulation study was performed to illustrate the parameter recovery. The results of an application to a real data set indicate that the new model performs very well in the presence of outliers compared with other alternatives in the literature.

29 January 2025 in Axioms
Verified
Journal Article
Open access
PDF file
HMHéctor Andrés Muñoz
Héctor Andrés Muñoz
JCJaime S. Castillo
Jaime S. Castillo
Diego Ignacio GallardoDiego Ignacio Gallardo
Diego Ignacio Gallardo
Osvaldo VenegasOsvaldo Venegas
Osvaldo Venegas
Héctor GómezHéctor Gómez
Héctor Gómez
In this work, we present a new distribution, which is a slash extension of the distribution of the sum of two independent Lindley random variables. This new distribution is developed using the slash methodology, resulting in a distribution with more flexible kurtosis, i.e., the ability to model atypical data. We study the density function of the new model and some of its properties, such as the cumulative distribution function, moments, and its asymmetry and kurtosis coefficients. The parameters are estimated by the maximum likelihood method with the EM algorithm. Finally, we apply the proposed model to two real datasets with high kurtosis, showing that it provides a better fit than two distributions known in the literature.