COWORDS: a probabilistic model for multiple word clouds

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.     

Stressful Life Events and Physical and Psychological Health: Mediating Effects of Differentiation of Self in a Spanish Sample

The pathways between differentiation of self and health remain only partly elucidated. This cross‐cultural study sought to test Bowen's hypothesis about the associations between differentiation, stressful life events, and physical and psychological health, in a sample of 466 Spanish adults. Results show that people with higher levels of differentiation were less prone to physical ailments (e.g., heart disease, cancer, or blood disorders) and psychological symptoms (e.g., depression or anxiety). Further, differentiation mediated the association between stress (i.e., perceived negativity of stress in the past year and number of stressful events across the lifetime) and physical and psychological health. The current research provides cross‐cultural empirical evidence for the links between differentiation and mind and body health in a Mediterranean culture.     

Are there electoral cycles of emigration? An empirical investigation based on European data

Economic literature suggests that economic factors and the availability of amenities act as determinants of migration choices together with socio‐demographic factors. Migration has also been found to be the consequence of political instability. This article argues that specific political events, i.e., democratic elections, may be linked to migration flows. By using European data over the 1999‐2012 time period, our system GMM estimations reveal that there is an emigration political cycle across European democracies and across the young democracies of Central and Eastern European countries. We observe that regular elections tend to diminish emigration ratios, whereas endogenous elections have the opposite effect. These results suggest special challenges for governments and oppositions, which are also discussed.     

The recruitment and selection of hospital medical consultants

Abstract

The literature on selection has been dominated in recent decades by the ‘prediction’ paradigm. Such an approach requires a substantial number of positions to be filled in order to compute validity coefficients. There are other approaches, using a different paradigm, when concentrated on adjusting the organization and the employee. Here careful analysis of job demands is essential. This article describes such an analysis, taking the medical consultant as an example. The method can be used in an iterative way. It is useful when one has to work with small numbers of positions.     

Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators

It is well-known that under fairly conditions linear regression becomes a powerful statistical tool. In practice, however, some of these conditions are usually not satisfied and regression models become ill-posed, implying that the application of traditional estimation methods may lead to non-unique or highly unstable solutions. Addressing this issue, in this paper a new class of maximum entropy estimators suitable for dealing with ill-posed models, namely for the estimation of regression models with small samples sizes affected by collinearity and outliers, is introduced. The performance of the new estimators is illustrated through several simulation studies.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

Dopamine Genes and Pathological Gambling in Discordant Sib-Pairs

Pathological gambling (PG) is an impulse control disorder that has been considered as a behavioral addiction. Recent studies have suggested the involvement of the dopaminergic system in addictions and impulse control disorders and associations of dopamine receptor genes (DRD1, DRD2, and DRD4) and PG have been reported. In the present study, 140 sib-pairs discordant for the diagnosis of PG (70 males and 70 females on each group) were recruited through the Gambling Outpatient Unit at the Institute of Psychiatry, University of Sao Paulo and were assessed by trained psychiatrists. A family-based association design was chosen to prevent population stratification. All subjects were genotyped for dopamine receptor genes (DRD1 -800 T/C, DRD2 TaqIA RFLP, DRD3 Ser9Gly, DRD4 48bp exon III VNTR, DRD5 (CA) repeat) and the dopamine transporter gene (SCL6A3 40 bp VNTR). Our results suggest the association of PG with DRD1 -800 T/C allele T (P = .03).