Examinando por Autor "Diego I. Gallardo"

Mostrando 1 - 2 de 2
  • No hay miniatura disponible
    Ítem
    A Modified Cure Rate Model Based on a Piecewise Distribution with Application to Lobular Carcinoma Data
    (2024) Yolanda M. Gómez; John L. Santibañez; Vinicius F. Calsavara; Héctor W. Gómez; Diego I. Gallardo
    A novel cure rate model is introduced by considering, for the number of concurrent causes, the modified power series distribution and, for the time to event, the recently proposed power piecewise exponential distribution. This model includes a wide variety of cure rate models, such as binomial, Poisson, negative binomial, Haight, Borel, logarithmic, and restricted generalized Poisson. Some characteristics of the model are examined, and the estimation of parameters is performed using the Expectation–Maximization algorithm. A simulation study is presented to evaluate the performance of the estimators in finite samples. Finally, an application in a real medical dataset from a population-based study of incident cases of lobular carcinoma diagnosed in the state of São Paulo, Brazil, illustrates the advantages of the proposed model compared to other common cure rate models in the literature, particularly regarding the underestimation of the cure rate in other proposals and the improved precision in estimating the cure rate of our proposal.
  • No hay miniatura disponible
    Ítem
    An Extension of the Akash Distribution: Properties, Inference and Application
    (2024) Yolanda M. Gómez; Luis Firinguetti Limone; Diego I. Gallardo; Héctor W. Gómez
    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.