Conflicts inherent in Corporate Codes

Codes of Conduct have enjoyed a long history as guides to human behavior. Corporate Codes of Conduct enjoy significant popularity in the U.S. and U.K. This article examines Codes in terms of their evolution (history), expectations (from possible to impossible), and enforcement (the area of greatest difficulty). The article examines trends in Corporate Codes and defines Codes as one aspect of developing corporate conscience. The article concludes with the view that useful as Codes may be, the inherent problem with Codes has been their separation over time from a moral foundation or philosophical base.     

Differences between actual and perceived student norms: an examination of alcohol use, drug use, and sexual behavior

Researchers in the social norms area have previously focused primarily on alcohol consumption, paying comparatively less attention to drug use and sexual behavior. The major purposes of this study were to (1) compare perceptions of peer norms in the areas of alcohol use, drug use, and sexual behavior with actual behavior and (2) determine if a relationship existed between a student's perceptions of normative behavior and a student's actual behavior. Participants were 833 college students at a large, public university in the Northeast. Study participants overestimated alcohol use, drug use, and sexual behavior among their peers. There was also a positive relationship between actual behavior and perceived peer norms, although the effect sizes for all behaviors were small to moderate. The authors provide further support for the tenets of social norms theory and suggest that social norms interventions are appropriate both campus-wide and to targeted high-risk groups.     

On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this performance is very similar to that of a parametric bootstrap correction based on a linear bias function. Three examples with actual data illustrate the application of our bias correction.     

Asymptotically Optimal Nonparametric Empirical Bayes Via Predictive Recursion

An empirical Bayes problem has an unknown prior to be estimated from data. The predictive recursion (PR) algorithm provides fast nonparametric estimation of mixing distributions and is ideally suited for empirical Bayes applications. This article presents a general notion of empirical Bayes asymptotic optimality, and it is shown that PR-based procedures satisfy this property under certain conditions. As an application, the problem of in-season prediction of baseball batting averages is considered. There the PR-based empirical Bayes rule performs well in terms of prediction error and ability to capture the distribution of the latent features.     

Bias reduction for the maximum likelihood estimator of the doubly-truncated Poisson distribution

ABSTRACT

We derive an analytic expression for the bias of the maximum likelihood estimator of the parameter in a doubly-truncated Poisson distribution, which proves highly effective as a means of bias correction. For smaller sample sizes, our method outperforms the alternative of bias correction via the parametric bootstrap. Bias is of little concern in the positive Poisson distribution, the most common form of truncation in the applied literature. Bias appears to be the most severe in the doubly-truncated Poisson distribution, when the mean of the distribution is close to the right (upper) truncation.     

An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures

Estimation of finite mixture models when the mixing distribution support is unknown is an important problem. This article gives a new approach based on a marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet prior model, a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented. By restricting the support to a finite grid, a simulated annealing method is employed to maximize the marginal likelihood and estimate the support. Real and simulated data examples show that this novel stochastic approximation and simulated annealing procedure compares favorably with existing methods.     

A Starting Point for Navigating the World of Daily Fantasy Basketball

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and K-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.     

Parents’ and children’s power effectiveness during polyadic family conflict: Process and outcome

Parent–child power effectiveness was investigated during naturally occurring polyadic family conflict with young children involving three or more family members. In 35/39 families, 210 conflict sequences were identified and coded for type of power, power effectiveness, and conflict outcome. Effective use of power overall and by each partner was assessed using two methods: microscopic (target’s response to an actor’s power move during the process of conflict) and macroscopic (power moves related to conflict outcome). Actor findings revealed that parents were more effective using certain types of power microscopically (i.e., simple, legitimate, reward) and macroscopically (i.e., simple, legitimate, questioning), whereas children were more effective using reward power macroscopically. Our findings support the unique qualities of the parent–child relationship and the complex context of polyadic family conflict. The study contributes to the literature on the socialization of young children in the family context.