Elder Abuse in Portugal: Findings From the First National Prevalence Study

In this study, we present findings of the Portuguese national prevalence study, “Aging and Violence,” the purpose of which was to estimate the prevalence of abuse and neglect of older people in family settings over a 12-month period and examine the relationship between abuse and sociodemographic and health characteristics. Through a telephone survey of a representative probability sample (N = 1,123), we evaluated 12 abusive behaviors and demographic data. Overall, 12.3% of older adults experienced elder abuse in family settings. The prevalence rates of specific types were as follows: psychological, 6.3%; financial, 6.3%; physical, 2.3%; neglect, 0.4%; and sexual, 0.2%. Logistic regression was employed to determine the relationship between abuse and covariates. The study suggests that education level, age, and functional status are significantly associated with abuse. Accurate estimates of the prevalence of elder abuse and understanding of victim and perpetrator characteristics are fundamental to designing effective strategies for prevention and intervention.     

Is the scheduling task context-dependent? A survey investigating the presence of constraints in different manufacturing contexts

Abstract

Most studies regarding real scheduling constraints only consider the constraints related to the specific manufacturing environment studied, limiting the possibility of drawing general conclusions. A survey of 50 companies was conducted in order to discover which constraints were present and what their relationship was with the scheduling context. This paper investigates which practical scheduling constraints are present in the manufacturing industry and whether the scheduling task is context-dependent. Results of this study show that some practical production constraints are context-dependent.     

On the Asymptotic Optimality of Empirical Likelihood for Testing Moment Restrictions

We show by example that empirical likelihood and other commonly used tests for moment restrictions are unable to control the (exponential) rate at which the probability of a Type I error tends to zero unless the possible distributions for the observed data are restricted appropriately. From this, it follows that for the optimality claim for empirical likelihood in Kitamura (2001) to hold, additional assumptions and qualifications are required. Under stronger assumptions than those in Kitamura (2001), we establish the following optimality result: (i) empirical likelihood controls the rate at which the probability of a Type I error tends to zero and (ii) among all procedures for which the probability of a Type I error tends to zero at least as fast, empirical likelihood maximizes the rate at which the probability of a Type II error tends to zero for most alternatives. This result further implies that empirical likelihood maximizes the rate at which the probability of a Type II error tends to zero for all alternatives among a class of tests that satisfy a weaker criterion for their Type I error probabilities.     

Estimating and Validating Nonlinear Regression Metamodels in Simulation

Frequently, the main objective of statistically designed simulation experiments is to estimate and validate regression metamodels, where the regressors are functions of the design variables and the dependent variable is the system response. In this article, a weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel is formulated and evaluated. Since the validity of a fitted regression model must be tested, a method for validating nonlinear regression simulation metamodels is presented. This method is a generalization of the cross-validation test proposed by Kleijnen (1983 Kleijnen , J. P. C. ( 1983 ). Cross-validation using the t statistic . European Journal of Operational Research 13 : 133 – 141 .[Crossref] , [Google Scholar]) in the context of linear regression metamodels. One drawback of the cross-validation strategy is the need to perform a large number of nonlinear regressions, if the number of experimental points is large. In this article, cross-validation is implemented using only one nonlinear regression. The proposed statistical analysis allows us to obtain Scheffé-type simultaneous confidence intervals for linear combinations of the metamodel's unknown parameters. Using the well-known M/M/1 example, a metamodel is built and validated with the aid of the proposed procedure.     

General mathematical properties,regression and applications of the log-gamma-generated family

The construction of some wider families of continuous distributions obtained recently has attracted applied statisticians due to the analytical facilities available for easy computation of special functions in programming software. We study some general mathematical properties of the log-gamma-generated (LGG) family defined by Amini, MirMostafaee, and Ahmadi (2014 Amini, M., S. M. T. K. MirMostafaee, and J. Ahmadi. 2014. Log-gamma-generated families of distributions. Statistics 48:913–32.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). It generalizes the gamma-generated class pioneered by Risti? and Balakrishnan (2012 Risti?, M. M., and N. Balakrishnan. 2012. The gamma exponentiated exponential distribution. Journal of Statistical Computation and Simulation 82:1191–206.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). We present some of its special models and derive explicit expressions for the ordinary and incomplete moments, generating and quantile functions, mean deviations, Bonferroni and Lorenz curves, Shannon entropy, Rényi entropy, reliability, and order statistics. Models in this family are compared with nested and non nested models. Further, we propose and study a new LGG family regression model. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models and therefore can be used more effectively in the analysis of survival data. We prove that the proposed models can provide consistently better fits in some applications to real data sets.     

Does country-risk influence electricity production worldwide?

This paper investigates the relationship between country-risks (or conflicts) within countries and electricity production. The determinants of electricity production have been neglected in the literature that favours the relationship between energy consumption, growth and development, and despite of the major challenges on electricity supply systems. We empirically establish that weighted conflict index, as well as sub-items as anti-government demonstrations, government crises and riots negatively influence electricity production per capita, after controlling for income per capita. Country conflicts affect electricity production mostly in the long-run. Our results imply that conflicts may affect electricity production substantially and consequently the economy as a whole. Policy should emphasize the institutional framework to avoid conflicts within countries in order to secure electricity production.     

A note on influence diagnostics in AR(1) time series models

The purpose of this paper is to develop influence diagnostics for AR(1) models under the innovative and the data perturbation schemes. There are four main contributions. First, we derive analytical expressions for the slope and curvature statistics. Second, we establish a relationship between the slope and curvature showing that the standardised slope and standardised curvature are equal for the innovative perturbation scheme, and these vectors are nearly identical for several values of the autoregressive parameter, for the data perturbation scheme. Third, we present a connection between the influence statistics and the tests for outlier detection. Fourth, for the innovative perturbation scheme, we derive the asymptotic distribution of a new influence statistic, whereas for the data perturbation scheme, the distribution of the influence statistics is obtained via Monte Carlo simulation. We additionally discuss practical guidelines for the use of local influence statistics, which are illustrated on a chemical process data set.     

Linking gender,class and race in Brazil1

This article examines the socio‐historical effects of the intersection of representations of gender, race, and the nation which determine black Brazilian women's subaltern social position and inform their self‐representations. This intersection is presented from two perspectives: analysis of quantitative data and a case study. Through the examination of socio‐economic data on black women participation in the labour market, it show that, when compared with white women and black men, black women are unequally concentrated in certain low paid jobs in the service industry, such as for instance domestic work. Through a brief case study, basically interviews with women from different colours and social classes — i.e. domestic workers and their employers — it shows how the particularities of domestic work inform black women's race and gender self‐definitions.     

Biodiversity conservation in urban parks: a study of ground-dwelling ants (Hymenoptera: Formicidae) in Rio de Janeiro City

Urban Ecosystems - Urban green recreational spaces may provide habitat for animals and plants. We investigated the importance of urban public parks in the city of Rio de Janeiro with regard to the...