Examinando por Autor "Venegas, Osvaldo"

Mostrando 1 - 10 de 10
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    Ítem
    A Composite Half-Normal-Pareto Distribution with Applications to Income and Expenditure Data
    (MDPI AG, 2024) Olmos, Neveka M.; Gómez-Déniz, Emilio; Venegas, Osvaldo; Gómez, Héctor W.
    The half-normal distribution is composited with the Pareto model to obtain a uni-parametric distribution with a heavy right tail, called the composite half-normal-Pareto distribution. This new distribution is useful for modeling positive data with atypical observations. We study the properties and the behavior of the right tail of this new distribution. We estimate the parameter using a method based on percentiles and the maximum likelihood method and assess the performance of the maximum likelihood estimator using Monte Carlo. We report three applications, one with simulated data and the others with income and expenditure data, in which the new distribution presents better performance than the Pareto distribution.
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    A power maxwell distribution with heavy tails and applications
    (MDPI, 2020) Segovia, Francisco A; Gómez, Yolanda M.; Venegas, Osvaldo; Gómez, Héctor W.
    In this paper we introduce a distribution which is an extension of the power Maxwell distribution. This new distribution is constructed based on the quotient of two independent random variables, the distributions of which are the power Maxwell distribution and a function of the uniform distribution (0,1) respectively. Thus the result is a distribution with greater kurtosis than the power Maxwell. We study the general density of this distribution, and some properties, moments, asymmetry and kurtosis coefficients. Maximum likelihood and moments estimators are studied. We also develop the expectation–maximization algorithm to make a simulation study and present two applications to real data.
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    An asymmetric bimodal distribution with application to quantile regression
    (MDPI, 2019) Gómez, Yolanda M; Gómez-Déniz, Emilio; Venegas, Osvaldo; Gallardo, Diego I.; Gómez, Héctor W.
    In this article, we study an extension of the sinh Cauchy model in order to obtain asymmetric bimodality. The behavior of the distribution may be either unimodal or bimodal. We calculate its cumulative distribution function and use it to carry out quantile regression. We calculate the maximum likelihood estimators and carry out a simulation study. Two applications are analyzed based on real data to illustrate the flexibility of the distribution for modeling unimodal and bimodal data
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    An asymmetric distribution with heavy tails and its expectation-maximization (EM) algorithm implementation
    (MDPI, 2019) Olmos, Nevenka M; Venegas, Osvaldo; Gómez, Yolanda M.; Iriarte, Yuri A.
    In this paper we introduce a new distribution constructed on the basis of the quotient of two independent random variables whose distributions are the half-normal distribution and a power of the exponential distribution with parameter 2 respectively. The result is a distribution with greater kurtosis than the well known half-normal and slashed half-normal distributions. We studied the general density function of this distribution, with some of its properties, moments, and its coefficients of asymmetry and kurtosis. We developed the expectation–maximization algorithm and present a simulation study. We calculated the moment and maximum likelihood estimators and present three illustrations in real data sets to show the flexibility of the new model.
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    Modified Slash Lindley Distribution
    (Hindawi, 2017) Reyes, Jimmy; Venegas, Osvaldo; Gómez, Héctor W.
  • No hay miniatura disponible
    Ítem
    Modified Unit-Half-Normal Distribution with Applications
    (MDPI, 2024) Alvarez, Paulina I.; Varela, Héctor; Cortés, Isaac E.; Venegas, Osvaldo; Gómez, Héctor W.
    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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    On properties of the bimodal skew-normal distribution and an application
    (MDPI, 2020) Elal-Olivero, David; Olivares-Pacheco, Juan F.; Venegas, Osvaldo; Bolfarine, Heleno; Gómez, Héctor W.
    The main object of this paper is to develop an alternative construction for the bimodal skew-normal distribution. The construction is based upon a study of the mixture of skew-normal distributions. We study some basic properties of this family, its stochastic representations and expressions for its moments. Parameters are estimated using the maximum likelihood estimation method. A simulation study is carried out to observe the performance of the maximum likelihood estimators. Finally, we compare the efficiency of the new distribution with other distributions in the literature using a real data set. The study shows that the proposed approach presents satisfactory results.
  • No hay miniatura disponible
    Ítem
    Reparameterized Scale Mixture of Rayleigh DistributionRegression Models with Varying Precision
    (MDPI, 2024) Rivera, Pilar A.; Gallardo, Diego I.; Venegas, Osvaldo; Gómez Déniz, Emilio; Gómez, Héctor W.
    In this paper, we introduce a new parameterization for the scale mixture of the Rayleigh distribution, which uses a mean linear regression model indexed by mean and precision parameters to model asymmetric positive real data. To test the goodness of fit, we introduce two residuals for the new model. A Monte Carlo simulation study is performed to evaluate the parameter estimation of the proposed model. We compare our proposed model with existing alternatives and illustrate its advantages and usefulness using Gilgais data in R software version 4.2.3 with the gamlss package
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    Scale Mixture of Exponential Distribution with an Application
    (MDPI, 2024) Barahona, Jorge A.; Gómez, Yolanda M.; Gómez Déniz, Emilio; Venegas, Osvaldo; Gómez, Héctor W.
    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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    Scale Mixture of Gleser Distribution with an Application toInsurance Data
    (MDPI, 2024) Olmos, Neveka M.; Gómez Déniz, Emilio; Venegas, Osvaldo
    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