Work of gravediggers and health

Gravediggers have death as object of their work. Their activities are painful, physically and mental demanding, as well as unhealthy. Literature is scarce about this theme. The aim of this study is to evaluate gravediggers' work activities and health consequences. The methodological frame which guided this study was Dejours' psychic suffering and its association with the psychodynamic aspects of work. Data collection took place in April-May 2011 in one public and one private cemetery of S?o Paulo, Brazil. Four male workers, aged between 45 to 60 years old were interviewed. Their work activities were observed during a workday. Participants reported their life dreams, defense mechanisms and frustration. The discourse of gravediggers showed serious problems associated to physical and mental demands, public invisibility and/ or social devaluation of work. The most important physical symptom was body pain. In spite this is a preliminary study, it was possible to raise a number of work stressors and health outcomes of gravediggers, an "invisible" worker of our society.     

Unconstrained parametrizations for variance-covariance matrices

The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness, thus leaving the estimation problem unconstrained. We compare the parametrizations based on their computational efficiency and statistical interpretability. The results described here are particularly useful in maximum likelihood and restricted maximum likelihood estimation in linear and non-linear mixed-effects models, but are also applicable to other areas of statistics.     

Wavelet‐based estimators for mixture regression

We consider a process that is observed as a mixture of two random distributions, where the mixing probability is an unknown function of time. The setup is built upon a wavelet‐based mixture regression. Two linear wavelet estimators are proposed. Furthermore, we consider three regularizing procedures for each of the two wavelet methods. We also discuss regularity conditions under which the consistency of the wavelet methods is attained and derive rates of convergence for the proposed estimators. A Monte Carlo simulation study is conducted to illustrate the performance of the estimators. Various scenarios for the mixing probability function are used in the simulations, in addition to a range of sample sizes and resolution levels. We apply the proposed methods to a data set consisting of array Comparative Genomic Hybridization from glioblastoma cancer studies.     

A novel quantification of information for longitudinal data analyzed by mixed‐effects modeling

Nonlinear mixed‐effects (NLME) modeling is one of the most powerful tools for analyzing longitudinal data especially under the sparse sampling design. The determinant of the Fisher information matrix is a commonly used global metric of the information that can be provided by the data under a given model. However, in clinical studies, it is also important to measure how much information the data provide for a certain parameter of interest under the assumed model, for example, the clearance in population pharmacokinetic models. This paper proposes a new, easy‐to‐interpret information metric, the “relative information” (RI), which is designed for specific parameters of a model and takes a value between 0% and 100%. We establish the relationship between interindividual variability for a specific parameter and the variance of the associated parameter estimator, demonstrating that, under a “perfect” experiment (eg, infinite samples or/and minimum experimental error), the RI and the variance of the model parameter estimator converge, respectively, to 100% and the ratio of the interindividual variability for that parameter and the number of subjects. Extensive simulation experiments and analyses of three real datasets show that our proposed RI metric can accurately characterize the information for parameters of interest for NLME models. The new information metric can be readily used to facilitate study designs and model diagnosis.     

Exchange rate regimes and the cross-country distribution of the 1997 financial crisis

We study variations in the severity of the 1997 financial crisis in a sample of 25 developing countries. We use both currency depreciation and stock market returns as crisis measures. Our key findings are that countries that started 1997 with an exchange rate peg experienced significantly greater currency depreciation and significantly lower stock returns than would be predicted from the levels of various macroeconomic indicators.     

Social Enterprise Performance: The Role of Market and Social Entrepreneurship Orientations

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Market orientation has been presented as an important predictor of business performance, and it is presumed to contribute...     

Comparison of Ability Estimation Methods Using IRT for Tests with Different Degrees of Difficulty

The aim of the present study is to determine the dependence of the estimation of individual abilities obtained by item response theory (IRT) in relation to the degree of test difficulty and to evaluate how the estimation error may be affected by the estimation method employed. It is shown that abilities in the scale region with little test information are more efficiently estimated using the maximum weighted likelihood estimation (WLE) method, particularly abilities belonging to the upper part of the scale. The study also demonstrates the importance of largest tests for ability estimation.