Bayesian density regression for discrete outcomes

We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.     

In Search of Executive Impairment in Pathological Gambling: A Neuropsychological Study on Non-treatment Seeking Gamblers

Pathological gambling is characterized by a persisting maladaptive and recurrent behavior with severe social and psychological consequences. There is evidence of strong comorbidity with psychiatric manifestations as well as cognitive mainly involving executive functions. This study aimed to investigate impairment in executive functions and working memory, and personality traits in a sample of Greek gamblers. Twenty-four men involved in various gambling activities were recruited from ecological settings as probable pathological gamblers. They were assessed with a comprehensive neuropsychological battery involving several executive tasks, the Zuckerman–Kuhlman Personality Questionnaire, the Hospital Anxiety Depression Scale, and the Difficulties in Emotion Regulation Scale. An age- and education-level matched group of 21 men without history of habitual gambling served as controls. As a group, gamblers displayed significantly lower scores on indices of inhibition, decision making and self-reported emotional awareness, and scored higher on impulsivity/sensation seeking personality traits. Notably, gamblers scored similarly or significantly higher on measures of verbal and visuospatial working memory, cognitive flexibility, processing speed, verbal fluency, and sustained attention. Overall, we argue that gamblers do present with specific cognitive deficits, but there is no evidence for a generalized executive impairment, and further stress the importance of investigating cognitive, personality, and psychiatric aspects of gambling on the basis of an ecologically valid sampling.     

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte Carlo ideas. The efficiency of standard SAA algorithm crucially depends on its self-adjusting mechanism which presents stability issues in high dimensional or rugged optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that significantly improves the stability of the self-adjusting mechanism and the search for the global optimum in the sampling space, as well as it inherits SAA desired convergence properties when a square-root cooling schedule is used. It can be implemented in parallel computing environments in order to mitigate the computational overhead. As a result, PISAA can address complex optimisation problems that it would be difficult for SAA to satisfactory address. We demonstrate the good performance of the proposed algorithm on challenging applications including Bayesian network learning and protein folding. Our numerical comparisons suggest that PISAA outperforms the simulated annealing, stochastic approximation annealing, and annealing evolutionary stochastic approximation Monte Carlo.     

Bivariate short distributions

The method of MML estimation for a univariate normal (Tiku 1967, 1973) is extended to a bivariate normal population. Thus, a theoretical foundation is given to the robust correlation coefficient proposed by Tiku and Balakrishnan (1986).     

On continuity of incomplete preferences

A weak (strict) preference relation is continuous if it has a closed (open) graph; it is hemicontinuous if its upper and lower contour sets are closed (open). If preferences are complete these four conditions are equivalent. Without completeness continuity in each case is stronger than hemicontinuity. This paper provides general characterizations of continuity in terms of hemicontinuity for weak preferences that are modeled as (possibly incomplete) preorders and for strict preferences that are modeled as strict partial orders. Some behavioral implications associated with the two approaches are also discussed.     

Adolescent Online Gambling in Cyprus: Associated School Performance and Psychopathology

This paper presents results from the study of gambling behaviors in a representative sample of Cypriot high-school students. The aim of the study was to ascertain epidemiology of adolescent online gambling in Cyprus and possible correlates. The sample consisted of 2,684 students (48.5 % boys, 51.5 % girls) from the first grades of junior (Gymnasium) and senior (Lyceum) high school. Our results indicate that gamblers presented with lower school achievement and related expectations while scoring consistently higher on measures of Internet addiction, parental bonding and psychopathology. Discriminant analysis revealed two profiles which explained the most variance in gambling behavior; the first profile that corresponded to more explained variance was the student with higher psychopathology, less prosocial behavior, higher Internet addiction score with higher frequencies of online activities and moderate levels of truancy and lower expectation of school achievement. The second profile was that of a student who reported less psychopathology, more prosocial behavior, less involvement with the Internet in general but skipped classes more and his prospects on finishing high school were even slimmer. These results will be utilized in the design of a comprehensive prevention program in an effort to combat online addictive behaviors.     

The Happiness Workout

This study tests for causality from exercise and physical activity to life satisfaction (LS) by applying an instrumental variable approach with the respondents’ perceived benefits of exercise participation as instruments. Using data across 25 countries from the Eurobarometer survey, our results confirm the positive association between exercise and LS. In terms of causality, the results indicate that being active increases LS for both gender, though more for men than women. One main reason for this relationship is that exercise is perceived as being pleasurable, something that policy-makers should keep in mind when designing programmes to get us off the sofa.     

Multivariate mixtures of normals with unknown number of components

We present full Bayesian analysis of finite mixtures of multivariate normals with unknown number of components. We adopt reversible jump Markov chain Monte Carlo and we construct, in a manner similar to that of Richardson and Green (1997), split and merge moves that produce good mixing of the Markov chains. The split moves are constructed on the space of eigenvectors and eigenvalues of the current covariance matrix so that the proposed covariance matrices are positive definite. Our proposed methodology has applications in classification and discrimination as well as heterogeneity modelling. We test our algorithm with real and simulated data.