The Role of Metacognition in Pathological Gambling: A Mediation Model

Pathological gambling involves multitudinous costs related to financial, legal, and public health care aspects, as well as to specific psychological disorders. Despite the overall evidence suggesting that comorbid disorders represent a risk factor for pathological gambling, there is scant evidence on the appropriate treatments for gamblers with such disorders. In this context, metacognitive therapy is an interesting approach because it considers psychological disorders as a result of the activation of perseverative cognitive processes and attentional strategies in response to inner events. Several studies report that metacognition is associated with different psychological problems. This study investigated the relationship among comorbid disorders, metacognition, and pathological gambling. 69 pathological gamblers at the first hospital admission and 58 controls drawn from general population (matched for age, gender, education) completed a battery of self report instruments: Symptom Checklist-90-R, Metacognition Questionnaire 30, South Oaks Gambling Scale. Compared to controls, pathological gamblers showed higher level of comorbid symptomatology and metacognition. Correlation analyses showed that: comorbid symptomatology and metacognition were positively and significantly correlated with pathological gambling; metacognition was positively and significantly associated with comorbid symptomatology. Mediation analysis indicated that dysfunctional metacognitive strategies could have an indirect effect on pathological gambling mediated by concurrent psychological disorders. These findings provide some implications for gambling treatment programs: pathological gamblers should be screened for psychiatric disorders, and metacognitive therapy could be considered a correct treatment of pathological gamblers. Metacognitive therapy might lead to the reduction of the pathological gambling by the diminishing of the concurrent psychological disorders.     

Some measures of kurtosis and their inference on large datasets

AStA Advances in Statistical Analysis - This paper deals with the estimation of kurtosis on large datasets. It aims at overcoming two frequent limitations in applications: first, Pearson's...     

On Bayesian learning via loss functions

We provide a decision theoretic approach to the construction of a learning process in the presence of independent and identically distributed observations. Starting with a probability measure representing beliefs about a key parameter, the approach allows the measure to be updated via the solution to a well defined decision problem. While the learning process encompasses the Bayesian approach, a necessary asymptotic consideration then actually implies the Bayesian learning process is best. This conclusion is due to the requirement of posterior consistency for all models and of having standardized losses between probability distributions. This is shown considering a specific continuous model and a very general class of discrete models.     

Bayesian inference for contingency tables with given marginals

Summary In this paper we introduce a class of prior distributions for contingency tables with given marginals. We are interested in the structrre of concordance/discordance of such tables. There is actually a minor limitation in that the marginals are required to assume only rational values. We do argue, though, that this is not a serious drawback for all applicatory purposes. The posterior and predictive distributions given anM-sample are computed. Examples of Bayesian estimates of some classical indices of concordance are also given. Moreover, we show how to use simulation in order to overcome some difficulties which arise in the computation of the posterior distribution.     

Bayesian analysis of dynamic magnetic resonance breast images

Summary.  We describe an integrated methodology for analysing dynamic magnetic resonance images of the breast. The problems that motivate this methodology arise from a collaborative study with a tumour institute. The methods are developed within the Bayesian framework and comprise image restoration and classification steps. Two different approaches are proposed for the restoration. Bayesian inference is performed by means of Markov chain Monte Carlo algorithms. We make use of a Metropolis algorithm with a specially chosen proposal distribution that performs better than more commonly used proposals. The classification step is based on a few attribute images yielded by the restoration step that describe the essential features of the contrast agent variation over time. Procedures for hyperparameter estimation are provided, so making our method automatic. The results show the potential of the methodology to extract useful information from acquired dynamic magnetic resonance imaging data about tumour morphology and internal pathophysiological features.     

A statistical framework for expression-based molecular classification in cancer

Summary. Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.     

Demographic and Predeparture Factors Associated with Drinking and Alcohol-Related Consequences for College Students Completing Study Abroad Experiences

Abstract

Objective: Study abroad students are at risk for increased and problematic drinking behavior. As few efforts have been made to examine this at-risk population, the authors predicted drinking and alcohol-related consequences abroad from predeparture and site-specific factors. Participants: The sample consisted of 339 students completing study abroad programs. Methods: Participants filled out online measures at predeparture, abroad, and at postreturn. Results: The authors found that drinking and consequences abroad were predicted by a number of factors, including demographics (eg, younger age, male sex, Greek affiliation, white ethnicity), student factors (eg, low GPA, major area of study), study abroad site factors (eg, apartment living abroad, study in Europe), predeparture levels of drinking and consequences, sensation seeking, and goals related to social gathering. Conclusions: Findings can be used to inform campus policies for admission to study abroad programs as well as assist in the development of interventions targeted toward preventing risk for students during abroad experiences.