Denise A. Botter

Additional Cardiovascular Risk Factors Associated with Excess Weigth in Children and Adolescents. The Belo Horizonte Heart Study

OBJECTIVE To examine the association of overweight and obesity with physical activity, blood pressure (BP) and serum lipid profiles. METHODS Epidemiologic investigation of 1,450 students, between the ages of 6 and 18, in the city of Belo Horizonte, MG. DATA weight, height, BP, skinfold thickness, waist circumference, physical activity, total cholesterol (TC), LDL-c, HDL-c, and dietary habits. RESULTS The prevalence rates for overweight and obesity were 8.4% and 3.1%, respectively. In relation to the students in the lower quartile (Q1) of the distribution of subscapular skinfold, the students in the upper quartile (Q4) presented a 3.7 times higher risk (odds ratio) of having elevated TC levels. Overweight and obese students had a 3.6 times higher risk of having elevated systolic blood pressure, and a 2.7 times higher risk of elevated diastolic blood pressure when compared to normal weight students. The less active students in the Q1 of distribution of MET presented a 3.8 times higher risk of having elevated TC levels compared to those who were more active (Q4). CONCLUSION Students who were overweight, obese or in the upper quartiles for other adiposity variables, as well as students with low levels of physical activity or a sedentary lifestyle presented higher blood pressure levels and a lipid profile indicative of an increased risk of developing atherosclerosis.

Exercise impact on sustained attention of ADHD children, methylphenidate effects

Attention deficit hyperactivity disorder (ADHD) is related to a deficiency of central catecholamines (CA) in cognitive, biochemical, and physical tests, and pharmaceutical intervention may have no effect if it is not accompanied by changes in the environment. The objective of our study was to test the hypothesis that central CA are responsible for the increase in speed reaction seen after physical activity (PA) and to measure the impact of high intensity PA on the sustained attention of 25 children diagnosed with ADHD consistent with the Disease Statistical Mental-IV (DSM-IV) criteria. It is possible that practicing sports assists in the management of the disorder. The children were divided between users (US) and non-users (NUS) of methylphenidate (MTP), and the groups were compared to evaluate the effect of the drug on cognition after PA. Post-exercise performance on Conner’s Continuous Performance Test-II (CPT) was not affected by MTP, we observed significant improvements in response time, and we saw normalization in the impulsivity and vigilance measures. These results suggest that the improvements in cognition after physical effort are not CA dependent. Additionally, our results suggest that children’s attention deficits can be minimized through PA irrespective of treatment with MTP. Additional studies are necessary to confirm that exercise mitigates the harmful symptoms of ADHD.

Improved Likelihood Ratio Tests for Dispersion Models

Summary In this paper we discuss improved likelihood ratio tests for both the parameters in the systematic component and the dispersion parameter in the class of dispersion models (Jorgensen, 1987a). General formulae for the expected likelihood ratio statistic are obtained explicitly in dispersion models, which generalize previous results by Cordeiro (1983, 1985, 1987) and Cordeiro & Paula (1989a). The practical use of the formulae is that we can derive closed-form Bartlett corrections for these models when the information matrix has a closed-form. Various Bartlett corrections are given for special models. The formulae have advantages for numerical purposes because they require only simple operations on matrices. Algebraically, they may be handled within computer systems such as REDUCE. Some numerical examples involving real data clarify the use of these formulae.

Improved estimators for generalized linear models with dispersion covariates

This paper addresses the issue of bias reduction of maximum likelihood estimators in generalized linear models with dispersion covariates. For this class of models, we derive general formulae for the second-order biases of maximum likelihood estimators of the linear and dispersion parameters, linear predictors, precision parameters and mean values. Our formulae cover many important and commonly used models and can be viewed as an extension of the results in Cordeiro and McCullagh (1991) and Cordeiro (1993)These formulae are easily implemented by means of supplementary weighted linear regressions. They are also simple enough to be used algebraically to obtain several closed-form expressions in special models. The practical use of such bias corrections is illustrated in a simulation study.

Second- and third-order bias reduction for one-parameter family models

In this paper we derive second and third order bias-corrected maximum likelihood estimates in general uniparametric models. We compare the corrected estimates and the usual maximum likelihood estimate in terms of their mean squared errors. We also obtain closed-form expressions for bias-corrected estimates in one-parameter exponential family models. Our results cover many important and commonly used distributions.

Diagnostic techniques in generalized estimating equations

We consider herein diagnostic methods for the quasi-likelihood regression models developed by Zeger and Liang [Zeger, S. L., Liang, K.-Y., 1986, Longitudinal data analysis for discrete and conti-nuous outcomes. Biometrics, 42, 121–130.] to analyse discrete and continuous longitudinal data. Our proposal generalises well-known measures (projection matrix, Cooks distance and standardised resi-duals) developed for independent responses. Moreover, half-normal probability plots with simulated envelopes were developed for assessing the adequacy of the fitted model when the marginal distributions belong to the exponential family. To obtain such a plot, correlated outcomes were generated by simulation using algorithms described in the literature. Finally, two applications were presented to illustrate the techniques.We consider herein diagnostic methods for the quasi-likelihood regression models developed by Zeger and Liang [Zeger, S. L., Liang, K.-Y., 1986, Longitudinal data analysis for discrete and conti-nuous outcomes. Biometrics, 42, 121–130.] to analyse discrete and continuous longitudinal data. Our proposal generalises well-known measures (projection matrix, Cooks distance and standardised resi-duals) developed for independent responses. Moreover, half-normal probability plots with simulated envelopes were developed for assessing the adequacy of the fitted model when the marginal distributions belong to the exponential family. To obtain such a plot, correlated outcomes were generated by simulation using algorithms described in the literature. Finally, two applications were presented to illustrate the techniques.

A regression model for special proportions

Credit cards are a financial product with special characteristics. Dividing the amount paid by the customer in a given month by the total bill results in a variable that is partly discrete and partly continuous, which we call the relative payment amount (RPA). This variable is discrete at 0, c and 1, and it is continuous in the open interval (c, 1). The 0<c<1 value is known and is given by the ratio between the value of the minimum payment and the full amount, and this value is not fixed for all customers. Thus, in practice, the RPA is a variable whose support of its distribution is non-constant across population units. In this work, we propose a regression model for the RPA. The model allows all of the unknown parameters of the conditional distribution of the response variable to be modelled as a function of the explanatory variables, and it also accounts for the non-constant known parameter c. The estimation of the parameters of this model is discussed, diagnostic analysis is addressed and closed-form expressions for the score function and for the Fisher’s information matrix are provided. Moreover, some results related to the non-constant nature of c are obtained, simulation studies are performed and an application using real credit card data is presented.

Second-order biases of maximum likelihood estimates in overdispersed generalized linear models

In this paper, we derive general formulae for second-order biases of maximum likelihood estimates in overdispersed generalized linear models, thus generalizing results by Cordeiro and McCullagh (J. Roy. Statist. Soc. Ser. B 53 (1991) 629), and Botter and Cordeiro (Statist. Comput. Simul. 62 (1998) 91). Our formulae cover many important and commonly used models and are easily implemented by means of supplementary weighted linear regressions. They are also simple enough to be used algebraically to obtain several closed-form expressions in special models. The practical use of such formulae is illustrated in a simulation study.

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro, Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995). We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994).

Bartlett corrections for generalized linear models with dispersion covariates

We present, in matrix notation, general Bartlett correction formulae for several hypotheses in generalized linear models with dispersion covariates. These results generalize previous work by Cordeiro (1983, 1993) who obtained Bartlett corrections for generalized linear models with known dispersion parameter and for multiplicative heteroscedastic normal models, respectively. The formulae derived are simple enough to be used analytically to obtain several closed form Bartlett corrections in a variety of important tests when the information matrix has a closed form. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. We give applications to some special models and discuss improved likelihood ratio tests.