Browsing by Author "Arellano Valle, Reinaldo Boris"
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- ItemA Bayesian approach to errors-in-variables beta regression(2018) Figueroa-Zuniga, Jorge; Carrasco, Jalmar M. F.; Arellano Valle, Reinaldo Boris; Ferrari, Silvia L. P.
- ItemA flexible class of parametric distributions for Bayesian linear mixed models(2019) Maleki, Mohsen; Wraith, Darren; Arellano Valle, Reinaldo Boris
- ItemA note on the Fisher information matrix for the skew-generalized-normal model(2013) Arellano Valle, Reinaldo Boris; Gómez Geraldo, Héctor; Salinas, Hugo S.
- ItemA note on the parameterization of multivariate skewed-normal distributions(2013) Castro Cepero, Luis Mauricio; San Martín, Ernesto; Arellano Valle, Reinaldo Boris
- ItemA skew-normal dynamic linear model and Bayesian forecasting(2019) Arellano Valle, Reinaldo Boris; Contreras Reyes, Javier E.; López Quintero, Freddy Omar; Valdebenito Sanhueza, Abel
- ItemA two-piece normal measurement error model(2020) Arellano Valle, Reinaldo Boris; Azzalini, A.; Ferreira, C. S.; Santoro Pizarro, Karol I.
- ItemAddressing non-normality in multivariate analysis using the t-distribution(2023) Osorio, Felipe; Galea Rojas, Manuel Jesús; Henríquez, Claudio; Arellano Valle, Reinaldo BorisThe main aim of this paper is to propose a set of tools for assessing non-normality taking into consideration the class of multivariate t-distributions. Assuming second moment existence, we consider a reparameterized version of the usual t distribution, so that the scale matrix coincides with covariance matrix of the distribution. We use the local influence procedure and the Kullback–Leibler divergence measure to propose quantitative methods to evaluate deviations from the normality assumption. In addition, the possible non-normality due to the presence of both skewness and heavy tails is also explored. Our findings based on two real datasets are complemented by a simulation study to evaluate the performance of the proposed methodology on finite samples.
- ItemBayesian mismeasurement t-models for censored responses(2016) Rocha, G.; Loschi, R.; Arellano Valle, Reinaldo Boris
- ItemBias reduction of maximum likelihood estimates for a modified skew-normal distribution(2016) Arrué, J.; Arellano Valle, Reinaldo Boris; Gómez, H.
- ItemChange Point Detection in The Skew-Normal Model Parameters(2013) Arellano Valle, Reinaldo Boris; Castro Cepero, Luis Mauricio; Loschi, Rosangela H.
- ItemConditioning on Uncertain Event: Extensions to Bayesian Inference(2002) Loschi, Rosangela Helena; Arellano Valle, Reinaldo Boris; Iglesias Zuazola, Pilar Loreto
- ItemErrors-in-variables beta regression models(2014) Carrasco, J.; Ferrari, S.; Arellano Valle, Reinaldo Boris
- ItemEstimation and diagnostic analysis in skew-generalized-normal regression models(2018) Ferreira, Clécio S.; Arellano Valle, Reinaldo Boris
- ItemFlexible Bayesian analysis of the von Bertalanff growth functions with the us of a log-skew-t distributions(2017) López, Freddy; Contreras, Javier; Wiff, Rodrigo; Arellano Valle, Reinaldo Boris
- ItemGeneralized Skew-Normal Negentropy and Its Application to Fish Condition Factor Time Series(2017) Arellano Valle, Reinaldo Boris; Contreras, J.; Stehlik, M.
- ItemGrowth estimates of cardinalfish (Epigonus crassicaudus) based on scale mixtures of skew-normal distributions(2013) Contreras Reyes, Javier E.; Arellano Valle, Reinaldo Boris
- ItemInference in flexible families of distributions with normal kernel(2013) Arellano Valle, Reinaldo Boris; Rocha, G.; Loschi, R.
- ItemInference in multivariate regression models with measurement errors(2023) Sandoval Moreno, Gabriela; Galea Rojas, Manuel Jesús; Arellano Valle, Reinaldo BorisMultivariate regression models are helpful in many fields. However, independent variables (covariates or predictors) could be measured with error. That implies the necessity of considering a new kind of model called Multivariate Regression Models with Measurement Error (MRMMEs). This paper aims to carry out a statistical analysis of these models. We include estimation, hypothesis testing, model assessment, and influence diagnostics. Furthermore, besides considering the classical assumption of the normal distribution, we use maximum likelihood for the whole inference process. Finally, we study the developed approach's performance through simulation experiments and re-analyze the human lung function dataset presented in the literature to illustrate the methodology.
- ItemL-statistics from multivariate unified skew-elliptical distributions(2014) Arellano Valle, Reinaldo Boris; Jamalizadeh, Ahad; Mahmoodian, H.; Balakrishnan, N.
- ItemMaximum a-posteriori estimation of autoregressive processes based on finite mixtures of scale-mixtures of skew-normal distributions(2017) Maleki, Mohsen; Arellano Valle, Reinaldo Boris