Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

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.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

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

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Influence Assessment in an Heteroscedastic Errors-in-Variables Model

The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Effects of an intervention in eating habits and physical activity in Japanese-Brazilian women with a high prevalence of metabolic syndrome in Bauru, São Paulo State, Brazil

We evaluated the impact of a lifestyle intervention on the cardiometabolic risk profile of women participating in the Study on Diabetes and Associated Diseases in the Japanese-Brazilian Population in Bauru. This was a non-controlled experimental study including clinical and laboratory values at baseline and after a 1-year intervention period. 401 Japanese-Brazilian women were examined (age 60.8±11.7 years), and 365 classified for metabolic syndrome (prevalence = 50.6%). Subjects with metabolic syndrome were older than those without (63.0±10.0 vs. 56.7±11.6 years, p < 0.01). After intervention, improvements in variables were found, except for C-reactive protein. Body mass index and waist circumference decreased, but adiposity reduction was more pronounced in the abdominal region (87.0±9.7 to 84.5±11.2cm, p < 0.001). Intervention-induced differences in total cholesterol, LDL, and post-challenge glucose were significant; women who lost more than 5% body weight showed a better profile than those who did not. The lifestyle intervention in Japanese-Brazilian women at high cardiometabolic risk improved anthropometric and laboratory parameters, but it is not known whether such benefits will persist and result in long-term reduction in cardiovascular events.

Blood Pressure and Hemodynamic Adaptations after a Training Program in Young Individuals with Down Syndrome

Background:Cardiovascular diseases affect people worldwide. Individuals with Down Syndrome (DS) have an up to sixteen-time greater risk of mortality from cardiovascular diseases.Objective:To evaluate the effects of aerobic and resistance exercises on blood pressure and hemodynamic variables of young individuals with DS.Methods:A total of 29 young individuals with DS participated in the study. They were divided into two groups: aerobic training (AT) (n = 14), and resistance training (TR) (n = 15). Their mean age was 15.7 ± 2.82 years. The training program lasted 12 weeks, and had a frequency of three times a week for AT and twice a week for RT. AT was performed in treadmill/ bicycle ergometer, at an intensity between 50%-70% of the HR reserve. RT comprised nine exercises with three sets of 12 repetition-maximum. Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MBP) and hemodynamic variables were assessed beat-to-beat using the Finometer device before/after the training program. Descriptive analysis, the Shapiro-Wilk test to check the normality of data, and the two-way ANOVA for repeated measures were used to compare pre- and post-training variables. The Pearson’s correlation coefficient was calculated to correlate hemodynamic variables. The SPSS version 18.0 was used with the significance level set at p < 0.05.Results:After twelve weeks of aerobic and/or resistance training, significant reductions in variables SBP, DBP and MBP were observed.Conclusion:This study suggests a chronic hypotensive effect of moderate aerobic and resistance exercises on young individuals with DS.

Human capital in growth regressions: how much difference does data quality make? An update and further results

We construct estimates of educational attainment for a sample of OECD countries using previously unexploited sources. We follow a heuristic approach to obtain plausible time profiles for attainment levels by removing sharp breaks in the data that seem to reflect changes in classification criteria. We then construct indicators of the information content of our series and a number of previously available data sets and examine their performance in several growth specifications. We find a clear positive correlation between data quality and the size and significance of human capital coefficients in growth regressions. Using an extension of the classical errors in variables model, we construct a set of meta-estimates of the coefficient of years of schooling in an aggregate Cobb-Douglas production function. Our results suggest that, after correcting for measurement error bias, the value of this parameter is well above 0.50.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Asymptotic robust inferences in the analysis of mean and covariance structures

Structural equation models are widely used in economic, socialand behavioral studies to analyze linear interrelationships amongvariables, some of which may be unobservable or subject to measurementerror. Alternative estimation methods that exploit different distributionalassumptions are now available. The present paper deals with issues ofasymptotic statistical inferences, such as the evaluation of standarderrors of estimates and chi--square goodness--of--fit statistics,in the general context of mean and covariance structures. The emphasisis on drawing correct statistical inferences regardless of thedistribution of the data and the method of estimation employed. A(distribution--free) consistent estimate of $\Gamma$, the matrix ofasymptotic variances of the vector of sample second--order moments,will be used to compute robust standard errors and a robust chi--squaregoodness--of--fit squares. Simple modifications of the usual estimateof $\Gamma$ will also permit correct inferences in the case of multi--stage complex samples. We will also discuss the conditions under which,regardless of the distribution of the data, one can rely on the usual(non--robust) inferential statistics. Finally, a multivariate regressionmodel with errors--in--variables will be used to illustrate, by meansof simulated data, various theoretical aspects of the paper.

Occurrence and Structure of Arbuscular Mycorrhizal Fungal Communities in Cassava after Cultivation of Cover Crops as Observed by the “PCR-DGGE” Technique

ABSTRACT Cassava (Manihot esculenta Crantz) is a highly mycotrophic crop, and prior soil cover may affect the density of arbuscular mycorrhizal fungi (AMFs), as well as the composition of the AMFs community in the soil. The aim of this study was to evaluate the occurrence and the structure of AMFs communities in cassava grown after different cover crops, and the effect of the cover crop on mineral nutrition and cassava yield under an organic farming system. The occurrence and structure of the AMFs community was evaluated through polymerase chain reaction (PCR) and denaturing gradient gel electrophoresis (DGGE). A randomized block experimental design was used with four replications. Six different cover crop management systems before cassava were evaluated: black oats, vetch, oilseed radish, intercropped oats + vetch, intercropped oats + vetch + oilseed radish, plus a control (fallow) treatment mowed every 15 days. Oats as a single crop or oats intercropped with vetch or with oilseed radish increased AMFs inoculum potential in soil with a low number of propagules, thus benefiting mycorrhizal colonization of cassava root. The treatments did not affect the structure of AMFs communities in the soil since the AMFs communities were similar in cassava roots in succession to different cover crops. AMFs colonization was high despite high P availability in the soil. The cassava crop yield was above the regional average, and P levels in the leaves were adequate, regardless of which cover crop treatments were used. One cover crop cycle prior to the cassava crop was not enough to observe a significant response in variables, P in plant tissue, crop yield, and occurrence and structure of AMFs communities in the soil. In the cassava roots in succession, the plant developmental stage affected the groupings of the structure of the AMF community.