A Gender-based Examination of Past-year Recreational Gamblers

Background Most adults gamble recreationally yet few studies have systematically investigated for gender-related differences in recreational gamblers.Methods Logistic regression analyses were performed on data from a nationally representative sample of respondents from the 1998 Gambling Impact and Behavior Study. Results Female gamblers versus non-gamblers were more likely to report use of alcohol and drugs. Male gamblers versus non-gamblers were more likely to report alcohol use and abuse/dependence, any substance abuse/dependence, and lifetime incarceration. An analysis of gambling by gender group interaction effects did not identify significant differences in health-related measures. Male as compared with female gamblers reported beginning gambling earlier, experiencing higher past-year maximal losses and wins, and finding favorite and engaging in different types of gambling.Conclusions Despite heavier gambling in male as compared with female recreational gamblers, similar mental health functioning was observed in female and male past-year recreational gamblers. Types of gambling problematic for men and women are reflected in the gambling preferences of recreational gamblers.     

Optimal detection of Fechner-asymmetry

We consider a general class of skewed univariate densities introduced by Fechner [1897. Kollectivmasslehre. Engleman, Leipzig], and derive optimal testing procedures for the null hypothesis of symmetry within that class. Locally and asymptotically optimal (in the Le Cam sense) tests are obtained, both for the case of symmetry with respect to a specified location as for the case of symmetry with respect to some unspecified location. Signed-rank based versions of these tests are also provided. The efficiency properties of the proposed procedures are investigated by a derivation of their asymptotic relative efficiencies with respect to the corresponding Gaussian parametric tests based on the traditional Pearson–Fisher coefficient of skewness. Small-sample performances under several types of asymmetry are investigated via simulations.     

Diagnostics for multivariate imputations

Summary.  We consider three sorts of diagnostics for random imputations: displays of the completed data, which are intended to reveal unusual patterns that might suggest problems with the imputations, comparisons of the distributions of observed and imputed data values and checks of the fit of observed data to the model that is used to create the imputations. We formulate these methods in terms of sequential regression multivariate imputation, which is an iterative procedure in which the missing values of each variable are randomly imputed conditionally on all the other variables in the completed data matrix. We also consider a recalibration procedure for sequential regression imputations. We apply these methods to the 2002 environmental sustainability index, which is a linear aggregation of 64 environmental variables on 142 countries.     

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the Type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the Type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.     

Reviewing Leadership Styles: Overlaps and the Need for a New ‘Full‐Range’ Theory

A central topic in leadership research concerns the impact of leadership style – the pattern of attitudes that leaders hold and behaviors they exhibit. Since the year 2000, several new leadership styles have been proposed to capture important missing aspects beyond the dominant charismatic/transformational and transactional framework. The authors review the emerging literature on these new styles – ideological leadership, pragmatic leadership, authentic leadership, ethical leadership, spiritual leadership, distributed leadership, and integrative public leadership – as well as the recent work on servant leadership. They also comment on the Ohio State studies on leadership, and then discuss the ways in which these many styles overlap with transformational leadership and each other, and issue a call to leadership researchers to collectively develop a new ‘full‐range’ model of leadership that encompasses and distills what is unique about these various styles. The authors argue that such an integrated full‐range model is necessary for research on leadership style to progress.     

The framing of nothing and the psychology of choice

Journal of Risk and Uncertainty - Zero outcomes are inconsequential in most models of choice. However, when disclosing zero outcomes they must be designated. It has been shown that a gamble is...     

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the ‘Net Benefit’ or the ‘win ratio’ have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan–Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.     

The Value of Supplier Information to Improve Management of a Retailer's Inventory

The distribution of lead time demand is essential for determining reorder points in inventory systems. Usually, the distribution of lead time demand is approximated directly. However, in some cases it may be worthwhile to take the demand per unit time and lead time into account, particularly when specific information is available. This paper deals with the situation where a supplier, who produces on order in fixed production cycles, provides information on the status of the coming production run. The retailer can use this information to gain insight into the lead-time process. A fixed order (s_vQ) strategy is presented, with a set of reorder points s_v depending on the time t until the first possible delivery, which is determined by the information of the supplier. A Markov model that analyzes a given (s_vQ) strategy is used to quantify the value of the information provided by the supplier. Some numerical examples show that the approach may lead to considerable cost savings compared to the traditional approach that uses only one single reorder point, based on a two-moments approximation. Using this numerical insight, the pros and cons of a more frequent exchange of information between retailers and suppliers can be balanced.