Graphical and Numerical Methods for Detecting Influential Observations in Complex Bingham Data

We describe methods to detect influential observations in a sample of pre-shapes when the underlying distribution is assumed to be complex Bingham. One of these methods is based on Cook's distance, which is derived from the likelihood of the complex Bingham distribution. Other method is related to the tangent space, which is based on the local influence for the multivariate normal distribution. A method to detect outliers is also explained. The application of the methods is illustrated in both a real dataset and a simulated sample.     

The Multi-dimensional Characteristics of Wellbeing: How Different Aspects of Wellbeing Interact and Do Not Interact with Each Other

Because wellbeing is a multi-varied and dynamic phenomenon, social scientists need to better understand how different aspects of people’s wellbeing are tied together and how these ties differ between individuals at one point in time and within individuals over time. The paper outlines a general model that considers the multidimensional structure of wellbeing. We utilize the potential of latent variable modelling on a unique Swedish database: the Panel Survey of Ageing and the Elderly (PSAE). An inclusive and flexible model that considers the interactions between semi-autonomous domains (material resources, health, psychosocial factors, lifestyle, etc.) is developed. Our empirical study is based on cross-sectional PSAE data and focuses on people 55 years or older (N = 5,374). The analysis takes advantage of recent developments in statistical theory in the field of latent variable modelling to bring about a more dynamic and theory confirming analysis of a multifaceted phenomenon such as wellbeing.     

Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form

In this article, we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.     

Simulating space-time random fields with nonseparable Gneiting-type covariance functions

Statistics and Computing - Two algorithms are proposed to simulate space-time Gaussian random fields with a covariance function belonging to an extended Gneiting class, the definition of which...     

Model averaging inconcentration–QT analyses

This article describes how a frequentist model averaging approach can be used for concentration–QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model. We noticed also few differences in terms of performance between the model averaging approach and the more classical model selection approach, but we believe that, despite both can be recommended in practice, the model averaging approach can be more appealing because of some deficiencies of model selection approach pointed out in the literature. We think that a model averaging or model selection approach should be systematically considered for conducting concentration–QT analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of hazard rates for dependent random variables

In this paper, we define and study a new notion for the comparison of the hazard rates of two random variables taking into account their mutual dependence. Properties, applications and the comparison for a data set are given.     

Testing Hypotheses in Nonparametric Models of Production

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estimator is proved to attain the faster convergence rate of the CRS-DEA estimator under CRS. Using a sample of U.S. commercial banks, we test and reject convexity of the production set, calling into question results from numerous banking studies that have imposed convexity assumptions. Supplementary materials for this article are available online.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

Fatigue management by truck drivers in real life situations: some suggestions to improve training

Truck driver fatigue is a major safety issue for truck drivers and the public in general. Although training prepares drivers to effectively operate a truck, it tends to minimize the importance of working constraints faced daily on-the-job and thus reduces its impact on safety and effectiveness. With experience, drivers develop skills to combat fatigue. Documenting these skills can contribute to improved training of apprentices. An ethnographic approach was used to better understand the real-life fatigue management skills of truck drivers. Participant observation was used to analyze the activity of apprentices in training and the activity of truck drivers at work. Observations indicated that training focused on time management and regulations, but did not prepare trainees to manage real-life constraints. Experienced drivers were not merely managing time; rather they were managing working constraints (including time) as a whole. To do so, they used two strategies: managing psycho-physical transformations and dynamic work planning. By integrating psycho-physical preoccupations into all aspects of work and by preparing future drivers to face real-life constraints, we could better train and prepare apprentices. Drivers do develop effective skills to combat fatigue which can improve training and better prepare future drivers to face daily constraints. These improvements can have a significant impact on fatigue and safety in the transportation industry.     

Gender differences in the relationship between intimate partner violence victimization and the perception of dating situations among college students

Although the prevalence and severity of dating violence among college students is well known, the relationship between past victimization and perceptions of future dating situations has not been examined. Using both qualitative and quantitative research methods, this study investigated gender differences in the relationship between intimate partner violence victimization and the perceptions of dating situations. The study found that the more psychological, physical, or sexual violence that was experienced by females, the more likely they perceived dating situations as inappropriate. Males, on the other hand, were more likely to report aggressive behaviors in dating situations only if victimized by sexual violence. Implications for professionals working with college students or community prevention programs are discussed.