Perinatal and early life factors associated with symptoms of depression in Brazilian children

Background: Few studies have been conducted on the association between perinatal and early life factors with childhood depression and results are conflicting. Our aim was to estimate the prevalence and perinatal and early life factors associated with symptoms of depression in children aged 7 to 11 years from two Brazilian birth cohorts. Methods: The study was conducted on 1444 children whose data were collected at birth and at school age, in 1994 and 2004/2005 in Ribeirao Preto, where they were aged 10-11 years and in 1997/98 and 2005/06 in Sao Luis, where children were aged 7-9 years. Depressive symptoms were investigated with the Child Depression Inventory (CDI), categorized as yes (score >= 20) and no (score < 20). Adjusted and non-adjusted prevalence ratios (PR) were estimated by Poisson regression with robust estimation of the standard errors. Results: The prevalence of depressive symptoms was 3.9% (95% CI = 2.5-5.4) in Ribeirao Preto and 13.7% (95% CI = 11.0-16.4) in Sao Luis. In the adjusted analysis, in Ribeirao Preto, low birth weight (PR = 3.98; 95% CI = 1.72-9.23), skilled and semi-skilled manual occupation (PR = 5.30; 95% CI = 1.14-24.76) and unskilled manual occupation and unemployment (PR = 6.65; 95% CI = 1.16-38.03) of the household head were risk factors for depressive symptoms. In Sao Luis, maternal schooling of 0-4 years (PR = 2.39; 95% CI = 1.31-4.34) and of 5 to 8 years (PR = 1.80; 95% CI = 1.08-3.01), and paternal age < 20 years (PR = 1.92; 95% CI = 1.02-3.61), were independent risk factors for depressive symptoms. Conclusions: The prevalence of depressive symptoms was much higher in the less developed city, Sao Luis, than in the more developed city, Ribeirao Preto, and than those reported in several international studies. Low socioeconomic level was associated with depressive symptoms in both cohorts. Low paternal age was a risk factor for depressive symptoms in the less developed city, Sao Luis, whereas low birth weight was a risk factor for depressive symptoms in the more developed city, Ribeirao Preto.

Influence diagnostics for elliptical semiparametric mixed models

In this paper we extend semiparametric mixed linear models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. Penalized likelihood equations are applied to derive the maximum penalized likelihood estimates (MPLEs) which appear to be robust against outlying observations in the sense of the Mahalanobis distance. A reweighed iterative process based on the back-fitting method is proposed for the parameter estimation and the local influence curvatures are derived under some usual perturbation schemes to study the sensitivity of the MPLEs. Two motivating examples preliminarily analyzed under normal errors are reanalyzed considering some appropriate elliptical errors. The local influence approach is used to compare the sensitivity of the model estimates.

Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

Robust modeling using the generalized epsilon-skew-t distribution

In this paper, an alternative skew Student-t family of distributions is studied. It is obtained as an extension of the generalized Student-t (GS-t) family introduced by McDonald and Newey [10]. The extension that is obtained can be seen as a reparametrization of the skewed GS-t distribution considered by Theodossiou [14]. A key element in the construction of such an extension is that it can be stochastically represented as a mixture of an epsilon-skew-power-exponential distribution [1] and a generalized-gamma distribution. From this representation, we can readily derive theoretical properties and easy-to-implement simulation schemes. Furthermore, we study some of its main properties including stochastic representation, moments and asymmetry and kurtosis coefficients. We also derive the Fisher information matrix, which is shown to be nonsingular for some special cases such as when the asymmetry parameter is null, that is, at the vicinity of symmetry, and discuss maximum-likelihood estimation. Simulation studies for some particular cases and real data analysis are also reported, illustrating the usefulness of the extension considered.

Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance

In this paper, we carry out robust modeling and influence diagnostics in Birnbaum-Saunders (BS) regression models. Specifically, we present some aspects related to BS and log-BS distributions and their generalizations from the Student-t distribution, and develop BS-t regression models, including maximum likelihood estimation based on the EM algorithm and diagnostic tools. In addition, we apply the obtained results to real data from insurance, which shows the uses of the proposed model. Copyright (c) 2011 John Wiley & Sons, Ltd.