This work proposes joint modeling of parameters in the biparametric exponential family, including heteroscedastic linear regression (non linear regression) models; with joint modeling of the mean and precision (the ...

Partially linear models are important tools in statistical modelling, combining the flexibility of non–parametric models and the simple interpretation of linear models. Monotonicity constraints appear naturally in certain ...

The early work of Zellner on the multivariate Student-t linear model has been extended to Bayesian inference for linear models with dependent non-normal error terms, particularly through various papers by Osiewalski, Steel ...

This work presents the basical theorics fundaments of the generalized linear models e the caracteristiscs of the univariate multinomial and binomial logistic regression analysis. Practical aplications of binomial regression ...

Linear regression models where the response variable is censored are often considered in statistical analysis. A parametric relationship between the response variable and covariates and normality of random errors are ...

Bayesian influence measures for linear regression models have been developed mostly for normal regression models with noninformative prior distributions for the unknown parameters. In this work we extend existing results ...

BACKGROUND: Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are ...