Examinando por Autor "Yuri A. Iriarte"

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    Ítem
    A Bimodal Extension of the Beta-Binomial Distributionwith Applications
    (2024) Jimmy Reyes; Josu Najera Zuloaga; Dae-Jin Lee; Jaime Arrué; Yuri A. Iriarte
    In this paper, we propose an alternative distribution to model count data exhibitinguni/bimodality. It arises as a weighted version of the beta-binomial distribution, which is defined bya parametric weight function that admits up to two modes for the resulting probability mass function.Like the baseline beta-binomial distribution, the proposed distribution performs well in modelingoverdispersed binomial data. Structural properties of the new distribution are studied. Raw momentsare derived, which are used to describe the dispersion behavior relative to the mean and the skewnessbehavior. Parameter estimation is carried out using the maximum likelihood method. A simulationstudy is conducted in order to illustrate the behavior of the estimators. Finally, two applicationsillustrating the usefulness of the proposal are presented.
  • Miniatura
    Ítem
    A Weighted Skew-Logistic Distribution with Applicationsto Environmental Data
    (2024) Isaac Cortés; Jimmy Reyes; Yuri A. Iriarte
    Skewness and bimodality properties are frequently observed when analyzing environmental data such as wind speeds, precipitation levels, and ambient temperatures. As an alternative to modeling data exhibiting these properties, we propose a flexible extension of the skew-logistic distribution. The proposal corresponds to a weighted version of the skewed logistic distribution, defined by a parametric weight function that allows shapes with up to three modes for the resulting density.Parameter estimation via the maximum likelihood approach is discussed. Simulation experiments are carried out to evaluate the performance of the estimators. Applications to environmental data illustrating the utility of the proposal are presented.