The Problem with Self-Forgiveness: Forgiving the Self Deters Readiness to Change Among Gamblers

Self-forgiveness is generally understood to be a mechanism that restores and improves the self. In the current study, we examine the possible deleterious consequences of forgiving the self among gamblers-specifically in regard to gamblers' readiness to change their problematic behavior. At a large Canadian university, 110 young adult gamblers' level of gambling pathology was assessed, along with their readiness to change and self-forgiveness for their gambling. Participants were 33 females and 75 males (2 unspecified) with a mean age of 20.33. Results revealed that level of pathology (at risk vs. problem gamblers) significantly predicted increased readiness to change. Self-forgiveness mediated this relationship, such that level of gambling pathology increased readiness to change to the extent that participants were relatively unforgiving of their gambling. Implications for seeking professional assistance as well as treatment and recovery are discussed.     

克拉多·帕塞拉：领导变革

迄今为止，很少有执行官能够成功地在一家陷入困境的大型组织内部实现变革，克拉多·帕塞拉就是为数不多的成功者之一，不仅如此，他还先后成功地领导两家大型企业完成了变革。     

Bivariate deconvolution with SIMEX: an application to mapping Alaska earthquake density

Constructing spatial density maps of seismic events, such as earthquake hypocentres, is complicated by the fact that events are not located precisely. In this paper, we present a method for estimating density maps from event locations that are measured with error. The estimator is based on the simulation–extrapolation method of estimation and is appropriate for location errors that are either homoscedastic or heteroscedastic. A simulation study shows that the estimator outperforms the standard estimator of density that ignores location errors in the data, even when location errors are spatially dependent. We apply our method to construct an estimated density map of earthquake hypocenters using data from the Alaska earthquake catalogue.     

Imputation and low-rank estimation with Missing Not At Random data

Statistics and Computing - Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on...