Examinando por Autor "Osvaldo Venegas"

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    Ítem
    Modified Unit-Half-Normal Distribution with Applications
    (2024) Paulina I. Alvarez; Héctor Varela; Isaac E. Cortés; Osvaldo Venegas; Héctor W. Gómez
    In this article, we introduce a new continuous distribution based on the unit interval. Thisdistribution is generated from a transformation of a random variable with half-normal distribution.We study its basic properties, percentiles, moments and order statistics. Maximum likelihoodestimation is applied, and we present a simulation study to observe the behavior of the maximumlikelihood estimators. We examine two applications to real proportions datasets, where the newdistribution is shown to provide a better fit than other distributions defined in the unit interval.
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    Ítem
    Scale Mixture of Exponential Distribution with an Application
    (2024) Jorge A. Barahona; Yolanda M. Gómez; Emilio Gómez Déniz; Osvaldo Venegas; Héctor W. Gómez
    This article presents an extended distribution that builds upon the exponential distribution.This extension is based on a scale mixture between the exponential and beta distributions. By utilizing this approach, we obtain a distribution that offers increased flexibility in terms of the kurtosis coefficient. We explore the general density, properties, moments, asymmetry, and kurtosis coefficients of this distribution. Statistical inference is performed using both the moments and maximum likelihood methods. To show the performance of this new model, it is applied to a real dataset with atypical observations. The results indicate that the new model outperforms two other extensions of the exponential distribution.
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    Ítem
    Scale Mixture of Gleser Distribution with an Application toInsurance Data
    (2024) Neveka M. Olmos; Emilio Gómez Déniz; Osvaldo Venegas
    In this paper, the scale mixture of the Gleser (SMG) distribution is introduced. This new distribution is the product of a scale mixture between the Gleser (G) distribution and the Beta(a, 1)distribution. The SMG distribution is an alternative to distributions with two parameters and a heavy right tail. We study its representation and some basic properties, maximum likelihood inference, and Fisher ’s information matrix. We present an application to a real dataset in which the SMG distribution shows a better fit than two other known distributions