Passion for poker and the relationship with gambling problems: a cross-sectional study

Previous research has shown that poker players spend significant time, large amounts of money and have a high prevalence of gambling problems. While some studies have examined the association between gambling behaviours and passion, none have explored this association specifically among poker players. This study aims to examine the relationships between two types of passion (i.e. harmonious and obsessive) and severity of gambling problems among poker players. A sample of 159 poker players was recruited from across Quebec, Canada. The outcome variable of interest was participants’ scores on the Canadian Pathological Gambling Index and the predictive variable was the Gambling Passion Scale. Multiple logistic regression analyses were conducted to identify independent risk factors of at-risk poker players. Severity of gambling problems is positively associated with obsessive passion (p < .05). However, harmonious passion is not associated with gambling problems. These results align with previous findings obtained for other gambling activities. Thus, passion could be considered a useful tool when approaching players to talk about risk-taking.     

Gambling Problems Among Community Cocaine Users

Cocaine use is highly prevalent and a major public health problem. While some studies have reported frequent comorbidity problems among cocaine users, few studies have included evaluation of gambling problems. This study aimed to estimate the prevalence of gambling problems and compare those who were at-risk gamblers with non-problem gamblers in terms of mental health problems, substance use problems, and some risk factors (i.e. family antecedents, erroneous perceptions and coping strategies) among individuals who smoke or inject cocaine. A total of 424 smoked or injected cocaine users recruited through community-based programs in Montreal (Quebec) completed the questionnaire, including the Canadian Pathological Gambling Index, the Composite International Diagnostic Interview, the CAGE, and the Severity Dependence Scale. Of the sample, 18.4 % were considered at-risk gamblers, of whom 7.8 % had problems gambling and 10.6 % were moderate-risk gamblers. The at-risk group was more likely to have experienced a recent phobic disorder and alcohol problems than the non-problem group. A multivariate analysis showed that, compared to those who were non-problem gamblers, the at-risk ones were more likely to have lost a large sum of money when they first started gambling, believed that their luck would turn, and gambled in reaction to painful life events. These results indicate the need to include routines for screening to identify gambling problem among cocaine users.     

Large Deviations Limit Theorems for the Kernel Density Estimator

We establish pointwise and uniform large deviations limit theorems of Chernoff-type for the non-parametric kernel density estimator based on a sequence of independent and identically distributed random variables. The limits are well-identified and depend upon the underlying kernel and density function. We derive then some implications of our results in the study of asymptotic efficiency of the goodness-of-fit test based on the maximal deviation of the kernel density estimator as well as the inaccuracy rate of this estimate     

Asymptotic normality of two symmetry test statistics based on the L1-error

In this paper, we propose two new tests to test the symmetry of a distribution. These tests are built up on the asymptotic normality of the L₁-distance to the symmetry of the Kernel and histogram density estimates. A simulation study is carried out to evaluate performances of the kernel based test.     

Rate of uniform consistency for a class of mode regression on functional stationary ergodic data

The aim of this paper is to study the asymptotic properties of a class of kernel conditional mode estimates whenever functional stationary ergodic data are considered. To be more precise on the matter, in the ergodic data setting, we consider a random elements (X, Z) taking values in some semi-metric abstract space \(E\times F\). For a real function \(\varphi \) defined on the space F and \(x\in E\), we consider the conditional mode of the real random variable \(\varphi (Z)\) given the event “\(X=x\)”. While estimating the conditional mode function, say \(\theta _\varphi (x)\), using the well-known kernel estimator, we establish the strong consistency with rate of this estimate uniformly over Vapnik–Chervonenkis classes of functions \(\varphi \). Notice that the ergodic setting offers a more general framework than the usual mixing structure. Two applications to energy data are provided to illustrate some examples of the proposed approach in time series forecasting framework. The first one consists in forecasting the daily peak of electricity demand in France (measured in Giga-Watt). Whereas the second one deals with the short-term forecasting of the electrical energy (measured in Giga-Watt per Hour) that may be consumed over some time intervals that cover the peak demand.     

On the asymptotic normality of the kernel estimators of the density function and its derivatives under censoring

In this paper, we study asymptotic normality of the kernel estimators of the density function and its derivatives as well as the mode in the randomly right censorship model. The mode estimator is defined as the random variable that maximizes the kernel density estimator. Our results are stated under some suitable conditions upon the kernel function, the smoothing parameter and both distributions functions that appear in this model. Here, the Kaplan–Meier estimator of the distribution function is used to build the estimates. We carry out a simulation study which shows how good the normality works.     

Rates of strong consistencies of the regression function estimator for functional stationary ergodic data

The aim of this paper is to study both the pointwise and uniform consistencies of the kernel regression estimate and to derive also rates of convergence whenever functional stationary ergodic data are considered. More precisely, in the ergodic data setting, we consider the regression of a real random variable Y over an explanatory random variable X taking values in some semi-metric separable abstract space. While estimating the regression function using the well-known Nadaraya-Watson estimator, we establish the strong pointwise and uniform consistencies with rates. Depending on the Vapnik-Chervonenkis size of the class over which uniformity is considered, the pointwise rate of convergence may be reached in the uniform case. Notice, finally, that the ergodic data framework extends the dependence setting to cases that are not covered by the usual mixing structures.     

One-Year Prospective Study on Passion and Gambling Problems in Poker Players

The concept of passion is relevant to understanding gambling behaviours and gambling problems. Longitudinal studies are useful to better understand the absence and development of gambling problems; however, only one study has specifically considered poker players. Using a longitudinal design, this study aims to determine the influence, 1 year later, of two forms of passion—harmonious and obsessive—on gambling problems in poker players. A total of 116 poker players was recruited from across Quebec, Canada. The outcome variable of interest was participants’ category on the Canadian Pathological Gambling Index, and the predictive variable was the Gambling Passion Scale. Multiple logistic regression analyses were conducted to identify independent risk factors of at-risk poker players 1 year later. Obsessive passion at baseline doubled the risk of gambling problems 1 year later (p < 0.01); for harmonious passion, there was no association. Number of gambling activities, drug problems, and impulsivity were also associated with at-risk gambling. This study highlights the links between obsessive passion and at-risk behaviours among poker players. It is therefore important to prevent the development of obsessive passion among poker players.     

Large deviations for the L1-distance in kernel density estimation

We establish a large deviation limit theorem of Chernoff type for the L₁-distance between the nonparametric kernel density estimator and the underlying density. The estimation is based on a sequence of independent and identically distributed random variables. The rate function is well identified, distribution-free and independent of the choice of the kernel.