Manuel Galea

Learn More
The aim of this paper is to derive local influence curvatures under various perturbation schemes for elliptical linear models with longitudinal structure. The elliptical class provides a useful generalization of the normal model since it covers both lightand heavy-tailed distributions for the errors, such as Student-t, power exponential, contaminated(More)
In this article, we develop influence diagnostic tools for the tobit model. Specifically, we discuss global influence methods based on the Cook distance and residuals with envelopes, and total and conformal local influence techniques. In order to analyze the sensitivity of the maximum likelihood estimators of the parameters of the model to small(More)
Functional heteroscedastic measurement error models are investigated aiming to assess the effects of perturbations of data on some inferential procedures. This goal is accomplished by resorting to methods of local influence. The techniques provide to the practitioner a valuable tool that enables to identify potential influential elements and to quantify the(More)
In this paper we consider applications of local influence (Cook, 1986) to evaluate small perturbations in the model or data set in the context of structural comparative calibration (Bolfarine and Galea, 1995) assuming that the measurements obtained follow a multivariate elliptical distribution. Different perturbation schemes are investigated and an(More)
In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal(More)
The Grubbs’ measurement model is frequently used to comparing several measuring devices. It is common to assume that the random terms have a normal distribution. However, such assumption makes the inference vulnerable to outlying observations whereas scale mixtures of normal distributions have been an interesting alternative to produce robust estimates(More)
The aim of this paper is to introduce generalized symmetric linear models (GSLMs) in the same sense of generalized linear models (GLMs), in which a link function is defined to establish a relationship between the mean values of symmetric distributions and linear predictors. The class of symmetric distributions contains various distributions with lighter and(More)