On estimating the box-cox transformation to normality

This paper studies four methods for estimating the Box-Cox parameter used to transform data to normality. Three of these are based on optimizing test statistics for standard normality tests (the Shapiro-Wilk. skewness, and kurtosis tests); the fourth uses the maximum likelihood estimator of the Box-Cox parameter. The four methods are compared and evaluated with a simulation study, where their performances under different skewness and kurtosis conditions are analyzed. The estimator based on optimizing the Shapiro-Wilk statistic generally gives rise to the best transformations, while the maximum likelihood estimator performs almost as well. Estimators based on optimizing skewness and kurtosis do not perform well in general.     

On a new goodness‐of‐fit process for families of copulas

A goodness‐of‐fit procedure is proposed for parametric families of copulas. The new test statistics are functionals of an empirical process based on the theoretical and sample versions of Spearman's dependence function. Conditions under which this empirical process converges weakly are seen to hold for many families including the Gaussian, Frank, and generalized Farlie–Gumbel–Morgenstern systems of distributions, as well as the models with singular components described by Durante [Durante ( 2007 ) Comptes Rendus Mathématique. Académie des Sciences. Paris, 344, 195–198]. Thanks to a parametric bootstrap method that allows to compute valid P‐values, it is shown empirically that tests based on Cramér–von Mises distances keep their size under the null hypothesis. Simulations attesting the power of the newly proposed tests, comparisons with competing procedures and complete analyses of real hydrological and financial data sets are presented. The Canadian Journal of Statistics 37: 80‐101; 2009 © 2009 Statistical Society of Canada     

The impact of sense of coherence and high-demand/low-control job environment on self-reported health, burnout and psychophysiological stress indicators

Sense of Coherence (SOC) is a new concept belonging to a salutogenic paradigm, proposing to explain health as contrasted to disease, a pathogenic paradigm. The Job Demand-Control (JDC) model of job stress suggests that the combination of high job demands and low job control, defined as job strain, is strongly associated with adverse health consequences. The aim of this study was to evaluate the relationship between SOC and the JDC model in assessment of negative job effects within three pathogenically defined contexts: self-reported health, burnout and psychophysiological stress indicators, assessing the explanatory value of SOC for such variables. The study was conducted with 103 employees of social-welfare and social-insurance agencies in Sweden. A questionnaire related to job conditions, health and burnout was administered, and blood samples were collected and analysed for serum concentrations of cortisol, prolactin and immunoglobulin G. Multiple-regression models were calculated including variables from all three contexts. In the analyses, a distinction was made between emotional job strain and quantitative job strain. The SOC interacted with emotional job strain, but the interaction also increased the independent effect of emotional job strain. The independent effect of SOC disappeared in most models when interaction was included. It is concluded that studies of job strain-effects according to the JDC model should include the SOC as an interaction factor.