Clécio S. Ferreira

Prolonged Ingestion of Prehydrolyzed Whey Protein Induces Little or No Change in Digestive Enzymes, but Decreases Glutaminase Activity in Exercising Rats

Because consumption of whey protein hydrolysates is on the increase, the possibility that prolonged ingestion of whey protein hydrolysates affect the digestive system of mammals has prompted us to evaluate the enzymatic activities of pepsin, leucine-aminopeptidase, chymotrypsin, trypsin, and glutaminase in male Wistar rats fed diets containing either a commercial whey isolate or a whey protein hydrolysate with medium degree of hydrolysis and to compare the results with those produced by physical training (sedentary, sedentary-exhausted, trained, and trained-exhausted) in the treadmill for 4 weeks. The enzymatic activities were determined by classical procedures in all groups. No effect due to the form of the whey protein in the diet was seen in the activities of pepsin, trypsin, chymotrypsin, and leucine-aminopeptidase. Training tended to increase the activity of glutaminase, but exhaustion promoted a decrease in the trained animals, and consumption of the hydrolysate decreased it even further. The results are consistent with the conclusion that chronic consumption of a whey protein hydrolysate brings little or no modification of the proteolytic digestive system and that the lowering of glutaminase activity may be associated with an antistress effect, counteracting the effect induced by training in the rat.

Psychometric evaluation of the Sociocultural Attitudes Towards Appearance Questionnaire-3 among Brazilian young adults.

The objective was evaluating the psychometric properties of the Sociocultural Attitudes Towards Appearance Questionnaire-3 (SATAQ-3) among Brazilian young adults of both genders. The sample was composed by 506 undergraduate students (295 females and 211 males), aged between 17 and 29 years old. Exploratory and confirmatory factor analyses were used for construct validity (N = 506). Correlations between the SATAQ-3 scores and those of the Tripartite Influence Scale (TIS) and Body Shape Questionnaire (BSQ) were used for convergent validity. Reliability was assessed through internal consistency (α) and reproducibility (test-retest) through comparison of the means obtained at two different time points and through intra-class correlation. The scale presented a factor structure composed of five factors, replicated in the confirmatory factor analysis with satisfactory values for the measurements of adjustment to the model. Correlations with the BSQ and TIS scores were rho = .52 and rho = -.35, respectively. Cronbachs alpha coefficients were satisfactory, and their stability was demonstrated. Brazilian SATAQ-3 had good validity and reproducibility, being indicated for use in samples of Brazilian youths.

Inference and diagnostics in skew scale mixtures of normal regression models

Skew scale mixtures of normal distributions are often used for statistical procedures involving asymmetric data and heavy-tailed. The main virtue of the members of this family of distributions is that they are easy to simulate from and they also supply genuine expectation-maximization (EM) algorithms for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models and we develop diagnostics analyses via local influence and generalized leverage, following Zhu and Lees approach. This is because Cooks well-known approach cannot be used to obtain measures of local influence. The EM-type algorithm has been discussed with an emphasis on the skew Student-t-normal, skew slash, skew-contaminated normal and skew power-exponential distributions. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.

The Mean-shift Outlier Model under Skew Normal Distribution

Asymmetric models have been extensively studied in recent years, in situations where the normality assumption is not satisfied due to lack of symmetry of the data. Techniques for assessing the quality of fit and diagnostic analysis are important for model validation. This paper presents a study of the mean-shift method for detecting outliers in asymmetric normal regression models. Analytical solutions for the estimators of the parameters are obtained using the algorithm. Simulation studies and application to real data are presented, showing the efficiency of the method in detecting outliers.Asymmetric models have been extensively studied in recent years, in situations where the normality assumption is not satisfied due to lack of symmetry of the data. Techniques for assessing the quality of fit and diagnostic analysis are important for model validation. This paper presents a study of the mean-shift method for detecting outliers in asymmetric normal regression models. Analytical solutions for the estimators of the parameters are obtained using the algorithm. Simulation studies and application to real data are presented, showing the efficiency of the method in detecting outliers.

Estimation and diagnostic analysis in skew-generalized-normal regression models

ABSTRACT The skew-generalized-normal distribution [Arellano-Valle, RB, Gómez, HW, Quintana, FA. A new class of skew-normal distributions. Comm Statist Theory Methods 2004;33(7):1465–1480] is a class of asymmetric normal distributions, which contains the normal and skew-normal distributions as special cases. The main virtues of this distribution is that it is easy to simulate from and it also supplies a genuine expectation–maximization (EM) algorithm for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models assuming skew-generalized-normal random errors and we develop a diagnostics analyses via local influence and generalized leverage, following Zhu and Lees approach. This is because Cooks well-known approach would be more complicated to use to obtain measures of local influence. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.

Scale and shape mixtures of multivariate skew-normal distributions

Abstract We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.

Estimation and diagnostic for skew-normal partially linear models

ABSTRACT Partially linear models (PLMs) are an important tool in modelling economic and biometric data and are considered as a flexible generalization of the linear model by including a nonparametric component of some covariate into the linear predictor. Usually, the error component is assumed to follow a normal distribution. However, the theory and application (through simulation or experimentation) often generate a great amount of data sets that are skewed. The objective of this paper is to extend the PLMs allowing the errors to follow a skew-normal distribution [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171–178], increasing the flexibility of the model. In particular, we develop the expectation-maximization (EM) algorithm for linear regression models and diagnostic analysis via local influence as well as generalized leverage, following [H. Zhu and S. Lee, Local influence for incomplete-data models, J. R. Stat. Soc. Ser. B 63 (2001), pp. 111–126]. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, a suitable transformation is applied in a data set on ragweed pollen concentration in order to fit PLMs under asymmetric distributions. An illustrative comparison is performed between normal and skew-normal errors.

Partially linear models and their applications to change point detection of chemical process data

ABSTRACT In many chemical data sets, the amount of radiation absorbed (absorbance) is related to the concentration of the element in the sample by Lambert–Beers law. However, this relation changes abruptly when the variable concentration reaches an unknown threshold level, the so-called change point. In the context of analytical chemistry, there are many methods that describe the relationship between absorbance and concentration, but none of them provide inferential procedures to detect change points. In this paper, we propose partially linear models with a change point separating the parametric and nonparametric components. The Schwarz information criterion is used to locate a change point. A back-fitting algorithm is presented to obtain parameter estimates and the penalized Fisher information matrix is obtained to calculate the standard errors of the parameter estimates. To examine the proposed method, we present a simulation study. Finally, we apply the method to data sets from the chemistry area. The partially linear models with a change point developed in this paper are useful supplements to other methods of absorbance–concentration analysis in chemical studies, for example, and in many other practical applications.

Mean-shift outliers model in skew scale-mixtures of normal distributions

ABSTRACT Asymmetric models have been discussed quite extensively in recent years, in situations where the normality assumption is suspected due to lack of symmetry in the data. Techniques for assessing the quality of fit and diagnostic analysis are important for model validation. This paper presents a study of the mean-shift method for the detection of outliers in regression models under skew scale-mixtures of normal distributions. Analytical solutions for the estimators of the parameters are obtained through the use of Expectation–Maximization algorithm. The observed information matrix for the calculation of standard errors is obtained for each distribution. Simulation studies and an application to the analysis of a data have been carried out, showing the efficiency of the proposed method in detecting outliers.