Examinando por Autor "Inmaculada Barranco Chamorro"

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    Ítem
    An Extension of the Fréchet Distribution and Applications
    (2024) Yolanda M. Gómez; Inmaculada Barranco Chamorro; Jaime S. Castillo; Héctor W. Gómez
    This paper presents the Slash-Exponential-Fréchet distribution, which is an expanded version of the Fréchet distribution. Through its stochastic representation, probability distribution function, moments and other relevant features are obtained. Evidence supports that the updated model displays a lighter right tail than the Fréchet model and is more flexible as for skewness and kurtosis. Results on maximum likelihood estimators are given. Our proposition’s applicability is demonstrated through a simulation study and the evaluation of two real-world datasets.
  • Miniatura
    Ítem
    New Flexible Asymmetric Log-Birnbaum–Saunders Nonlinear Regression Model with Diagnostic Analysis
    (2024) Guillermo Martínez Flórez; Inmaculada Barranco Chamorro; Héctor W. Gómez
    A nonlinear log-Birnbaum–Saunders regression model with additive errors is introduced. It is assumed that the error term follows a flexible sinh-normal distribution, and therefore it can be used to describe a variety of asymmetric, unimodal, and bimodal situations. This is a novelty since there are few papers dealing with nonlinear models with asymmetric errors and, even more, there are few able to fit a bimodal behavior. Influence diagnostics and martingale-type residuals are proposed to assess the effect of minor perturbations on the parameter estimates, check the fitted model, and detect possible outliers. A simulation study for the Michaelis–Menten model is carried out, covering a wide range of situations for the parameters. Two real applications are included, where the use of influence diagnostics and residual analysis is illustrated.
  • Miniatura
    Ítem
    Unit-Power Half-Normal Distribution Including QuantileRegression with Applications to Medical Data
    (2024) Karol I. Santoro; Yolanda M. Gómez; Darlin Soto; Inmaculada Barranco Chamorro
    In this paper, we present the unit-power half-normal distribution, derived from the power half-normal distribution, for data analysis in the open unit interval. The statistical properties of the unit-power half-normal model are described in detail. Simulation studies are carried out to evaluate the performance of the parameter estimators. Additionally, we implement the quantile regression for this model, which is applied to two real healthcare data sets. Our findings suggest that the unitpower half-normal distribution provides a robust and flexible alternative for existing models for proportion data