Felipe Osorio

Assessment of local influence in elliptical linear models with longitudinal structure

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 light- and heavy-tailed distributions for the errors, such as Student-t, power exponential, contaminated normal, among others. It is well known that elliptical models with longer-than-normal tails may present robust parameter estimates against outlying observations. However, little has been investigated on the robustness aspects of the parameter estimates against perturbation schemes. We use appropriate derivative operators to express the normal curvatures in tractable forms for any correlation structure. Estimation procedures for the position and variance-covariance parameters are also presented. A data set previously analyzed under a normal linear mixed model is reanalyzed under elliptical models. Local influence graphics are used to select less sensitive models with respect to some perturbation schemes.

Estimation in nonlinear mixed-effects models using heavy-tailed distributions

Nonlinear mixed-effects models are very useful to analyze repeated measures data and are used in a variety of applications. Normal distributions for random effects and residual errors are usually assumed, but such assumptions make inferences vulnerable to the presence of outliers. In this work, we introduce an extension of a normal nonlinear mixed-effects model considering a subclass of elliptical contoured distributions for both random effects and residual errors. This elliptical subclass, the scale mixtures of normal (SMN) distributions, includes heavy-tailed multivariate distributions, such as Student-t, the contaminated normal and slash, among others, and represents an interesting alternative to outliers accommodation maintaining the elegance and simplicity of the maximum likelihood theory. We propose an exact estimation procedure to obtain the maximum likelihood estimates of the fixed-effects and variance components, using a stochastic approximation of the EM algorithm. We compare the performance of the normal and the SMN models with two real data sets.

A chemometrics approach to analyze volatile molecules released by post-mortem bovine fast-twitch muscles

ABSTRACT It is well known that beef produces volatile molecules. In this work, the detection of volatiles released by post-mortem bovine fast-twitch muscles (Musculus longissimus dorsi and Musculus cutaneus trunci) was done using GC/MS–SPME (gas chromatography/mass spectrum–solid-phase microextraction). The releases of volatile molecules were modeled against three factors (rigor-mortis, animal age and oxidative capacity) using a chemometrics approach (experimental design and partial least squares regression). The GC/MS–SPME technique produced more than 30 reproducible chromatographic peaks, but only 13 were associated significantly with two factors (rigor-mortis and animal age). The volatile profile was composed mainly of alcohols, aldehydes and alkanes. The factor “animal age” was the main variable related to the release of volatile molecules. The results strongly suggest that the release of volatile molecules change according to post-mortem metabolism and the animal age.

Prediction of the economic activity from the short and long-term interest rate differential: new evidences in Chile and the United States of America cases

The purpose of this work is to verify the stability of the relationship between real activity and interest rate spread. The test is based on Chen (1988) and Osorio and Galea (2006). The analysis is applied to Chile and the United States, from 1980 to 1999. In general, in both cases the relationship was statistically significant in early 80s, but a break point is found in both countries during that decades, suggesting that the relationship depends on the monetary rule follow by the Central Bank.