Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

Iron Concentrations in Breast Milk and Selected Maternal Factors of Human Milk Bank Donors

The aim of this study was to evaluate the relationship between iron concentration in mature breast milk and characteristics of 136 donors of a Brazilian milk bank. Iron, vitamin A, zinc, and copper concentrations were assessed in human milk and maternal blood. Data were collected on maternal anthropometrics, obstetric, socioeconomic, demographic, and lifestyle factors. Iron, zinc, and copper in milk and zinc and copper in blood were detected by spectrophotometry. Vitamin A in milk and blood was determined by high-performance liquid chromatography. Hemoglobin was measured by electronic counting and serum iron and ferritin by colorimetry and chemoluminescence, respectively. Transferrin and ceruloplasmin were determined by nephelometry. According to multivariate linear regression analysis, iron in milk was positively associated with vitamin A in milk and with smoking but negatively associated with timing of breast milk donation (P < .001). These results indicate that iron concentration in milk of Brazilian donors may be influenced by nutritional factors and smoking. J Hum Lact. 26(2):175-179

The impact of maternal cortisol concentrations on child arterial elasticity

Epidemiological studies suggest that glucocorticoid excess in the fetus may contribute to the pathophysiology of cardiovascular diseases in adulthood. However, the impact of maternal glucocorticoid on the cardiovascular system of the offspring has not been much explored in studies involving humans, especially in childhood. The objective of this study was to assess the influence of maternal cortisol concentrations on child arterial elasticity. One hundred and thirty pregnant women followed from 1997 to 2000, and respective children 5-7 years of age followed from 2004 to 2006 were included in the study. Maternal cortisol was determined in saliva by an enzyme immunoassay utilizing the mean concentration of nine samples of saliva. Arterial elasticity was assessed by the large artery elasticity index (LAEI; the capacitive elasticity of large arteries) by recording radial artery pulse wave, utilizing the equipment HDI/PulseWave CR-2000 Cardiovascular Profiling System (R). The nutritional status of the children was determined by the body mass index (BMI). Insulin concentration was assessed by chemiluminescence, and insulin resistance by the homeostasis model assessment. Blood glucose, total cholesterol and fractions (LDL-c and HDL-c) and triglyceride concentrations were determined by automated enzymatic methods. The association between maternal cortisol and child arterial elasticity was assessed by multivariate linear regression analysis. There was a statistically significant association between maternal cortisol and LAEI (P=0.02), controlling for birth weight, age, BMI and HDL-c of the children. This study suggests that exposure to higher glucocorticoid concentrations in the prenatal period is associated to lower arterial elasticity in childhood, an earlier cardiovascular risk marker.

Factors associated with health-related quality of life after successful kidney transplantation: a population-based study

Kidney transplantation improves the quality of life of end-stage renal disease patients. The quality of life benefits, however, pertain to patients on average, not to all transplant recipients. The aim of this study was to identify factors associated with health-related quality of life after kidney transplantation. Population-based study with a cross-sectional design was carried out and quality of life was assessed by SF-36 Health Survey Version 1. A multivariate linear regression model was constructed with sociodemographic, clinical and laboratory data as independent variables. Two hundred and seventy-two kidney recipients with a functioning graft were analyzed. Hypertension, diabetes, higher serum creatinine and lower hematocrit were independently and significantly associated with lower scores for the SF-36 oblique physical component summary (PCSc). The final regression model explained 11% of the PCSc variance. The scores of oblique mental component summary (MCSc) were worse for females, patients with a lower income, unemployed and patients with a higher serum creatinine. The regression model explained 9% of the MCSc variance. Among the studied variables, comorbidity and graft function were the main factors associated with the PCSc, and sociodemographic variables and graft function were the main determinants of MCSc. Despite comprehensive, the final regression models explained only a little part of the heath-related quality of life variance. Additional factors, such as personal, environmental and clinical ones might influence quality of life perceived by the patients after kidney transplantation.

Multicenter Analysis of Glucocerebrosidase Mutations in Parkinson`s Disease

Background Recent studies indicate an increased frequency of mutations in the gene encoding glucocerebrosidase (GBA), a deficiency of which causes Gaucher`s disease, among patients with Parkinson`s disease. We aimed to ascertain the frequency of GBA mutations in an ethnically diverse group of patients with Parkinson`s disease. Methods Sixteen centers participated in our international, collaborative study: five from the Americas, six from Europe, two from Israel, and three from Asia. Each center genotyped a standard DNA panel to permit comparison of the genotyping results across centers. Genotypes and phenotypic data from a total of 5691 patients with Parkinson`s disease (780 Ashkenazi Jews) and 4898 controls (387 Ashkenazi Jews) were analyzed, with multivariate logistic-regression models and the Mantel-Haenszel procedure used to estimate odds ratios across centers. Results All 16 centers could detect two GBA mutations, L444P and N370S. Among Ashkenazi Jewish subjects, either mutation was found in 15% of patients and 3% of controls, and among non-Ashkenazi Jewish subjects, either mutation was found in 3% of patients and less than 1% of controls. GBA was fully sequenced for 1883 non-Ashkenazi Jewish patients, and mutations were identified in 7%, showing that limited mutation screening can miss half the mutant alleles. The odds ratio for any GBA mutation in patients versus controls was 5.43 across centers. As compared with patients who did not carry a GBA mutation, those with a GBA mutation presented earlier with the disease, were more likely to have affected relatives, and were more likely to have atypical clinical manifestations. Conclusions Data collected from 16 centers demonstrate that there is a strong association between GBA mutations and Parkinson`s disease.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

Bayesian analysis of skew-t multivariate null intercept measurement error model

The multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Twofold adaptive partition of unity implicits

Partition of Unity Implicits (PUI) has been recently introduced for surface reconstruction from point clouds. In this work, we propose a PUI method that employs a set of well-observed solutions in order to produce geometrically pleasant results without requiring time consuming or mathematically overloaded computations. One feature of our technique is the use of multivariate orthogonal polynomials in the least-squares approximation, which allows the recursive refinement of the local fittings in terms of the degree of the polynomial. However, since the use of high-order approximations based only on the number of available points is not reliable, we introduce the concept of coverage domain. In addition, the method relies on the use of an algebraically defined triangulation to handle two important tasks in PUI: the spatial decomposition and an adaptive polygonization. As the spatial subdivision is based on tetrahedra, the generated mesh may present poorly-shaped triangles that are improved in this work by means a specific vertex displacement technique. Furthermore, we also address sharp features and raw data treatment. A further contribution is based on the PUI locality property that leads to an intuitive scheme for improving or repairing the surface by means of editing local functions.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Some restriction tests in a new class of regression models for proportions

The main purpose of this work is to study the behaviour of Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-32] adjusted likelihood ratio statistic in testing simple hypothesis in a new class of regression models proposed here. The proposed class of regression models considers Dirichlet distributed observations, and the parameters that index the Dirichlet distributions are related to covariates and unknown regression coefficients. This class is useful for modelling data consisting of multivariate positive observations summing to one and generalizes the beta regression model described in Vasconcellos and Cribari-Neto [Vasconcellos, K.L.P., Cribari-Neto, F., 2005. Improved maximum likelihood estimation in a new class of beta regression models. Brazilian journal of Probability and Statistics 19,13-31]. We show that, for our model, Skovgaard`s adjusted likelihood ratio statistics have a simple compact form that can be easily implemented in standard statistical software. The adjusted statistic is approximately chi-squared distributed with a high degree of accuracy. Some numerical simulations show that the modified test is more reliable in finite samples than the usual likelihood ratio procedure. An empirical application is also presented and discussed. (C) 2009 Elsevier B.V. All rights reserved.