Examinando por Autor "Gallardo, Diego I."

Mostrando 1 - 6 de 6
  • Miniatura
    Ítem
    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 Extension of the Akash Distribution: Properties, Inference and Application
    (MDPI, 2024) Gómez, Yolanda M.; Firinguetti Limone, Luis; Gallardo, Diego I.; Gómez, Héctor W.
    In this article we introduce an extension of the Akash distribution. We use the slash methodology to make the kurtosis of the Akash distribution more flexible. We study the general probability density function of this new model, some properties, moments, skewness and kurtosis coefficients. Statistical inference is performed using the methods of moments and maximum likelihood via the EM algorithm. A simulation study is carried out to observe the behavior of the maximum likelihood estimator. An application to a real data set with high kurtosis is considered, where it is shown that the new distribution fits better than other extensions of the Akash distribution.
  • Miniatura
    Ítem
    Generalized modified slash Birnbaum-Saunders distribution
    (MDPI, 2018) Reyes, Jimmy; Barranco-Chamorro, Inmaculada; Gallardo, Diego I.; Gómez, Héctor W.
    In this paper, a generalization of the modified slash Birnbaum–Saunders (BS) distribution is introduced. The model is defined by using the stochastic representation of the BS distribution, where the standard normal distribution is replaced by a symmetric distribution proposed by Reyes et al. It is proved that this new distribution is able to model more kurtosis than other extensions of BS previously proposed in the literature. Closed expressions are given for the pdf (probability density functio), along with their moments, skewness and kurtosis coefficients. Inference carried out is based on modified moments method and maximum likelihood (ML). To obtain ML estimates, two approaches are considered: Newton–Raphson and EM-algorithm. Applications reveal that it has potential for doing well in real problems.
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    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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    Skewness of maximum likelihood estimators in the weibull censored data
    (MDPI, 2019) Magalhães, Tiago M; Gallardo, Diego I.; Gómez, Héctor W.
    In this paper, we obtain a matrix formula of order n−1/2, where n is the sample size, for the skewness coefficient of the distribution of the maximum likelihood estimators in the Weibull censored data. The present result is a nice approach to verify if the assumption of the normality of the regression parameter distribution is satisfied. Also, the expression derived is simple, as one only has to define a few matrices. We conduct an extensive Monte Carlo study to illustrate the behavior of the skewness coefficient and we apply it in two real datasets.
  • Miniatura
    Ítem
    Truncated power-normal distribution with application to non-negative measurements
    (MDPI, 2018) Castillo, Nabor O.; Gallardo, Diego I.; Bolfarine, Heleno; Gómez, Héctor W.
    This paper focuses on studying a truncated positive version of the power-normal (PN) modelconsidered in Durrans (1992). The truncation point is considered to be zero so that the resulting model is an extension of the half normal distribution. Some probabilistic properties are studied for the proposed model along with maximum likelihood and moments estimation. The model is fitted to two real datasets and compared with alternative models for positive data. Results indicate good performance of the proposed mode