Browsing by Author "Bolfarine, Heleno"

Now showing 1 - 6 of 6
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    A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem
    (TAYLOR & FRANCIS INC, 2010) Paz Casanova, Maria; Iglesias, Pilar; Bolfarine, Heleno
    In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.
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    A multivariate ultrastructural errors-in-variables model with equation error
    (ELSEVIER INC, 2011) Patriota, Alexandre G.; Bolfarine, Heleno; Arellano Valle, Reinaldo B.
    This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved
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    An Alternative to the Log-Skew-Normal Distribution: Properties, Inference, and an Application to Air Pollutant Concentrations
    (2022) Arrue, Jaime; Arellano-Valle, Reinaldo Boris; Venegas, Osvaldo; Bolfarine, Heleno; Gomez, Hector W.
    In this study, we consider an alternative to the log-skew-normal distribution. It is called the modified log-skew-normal distribution and introduces greater flexibility in the skewness and kurtosis parameters. We first study several of the main probabilistic properties of the new distribution, such as the computation of its moments and the non-existence of the moment-generating function. Parameter estimation by the maximum likelihood approach is also studied. This approach presents an overestimation problem in the shape parameter, which in some cases, can even be infinite. However, as we demonstrate, this problem is solved by adapting bias reduction using Firth's approach. We also show that the modified log-skew-normal model likewise inherits the non-singularity of the Fisher information matrix of the untransformed model, when the shape parameter is null. Finally, we apply the modified log-skew-normal model to a real example related to pollution data.
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    Bayesian inference in measurement error models for replicated data
    (2013) De Castro, Mario; Bolfarine, Heleno; Galea Rojas, Manuel Jesús
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    Robust linear functional mixed models
    (2015) Riquelme, Marco; Bolfarine, Heleno; Galea Rojas, Manuel Jesús
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    Semiparametric Bayesian measurement error modeling
    (ELSEVIER INC, 2010) Casanova, Maria P.; Iglesias, Pilar; Bolfarine, Heleno; Salinas, Victor H.; Pena, Alexis
    This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.