Control charts for monitoring the median in non-negative asymmetric data

Statistical Methods & Applications - Control charts are commonly used for monitoring the mean of processes. However, there are practical applications in which asymmetric data are the standard....     

Slack-variable models versus Scheffé's mixture models

Slack-variable models are compared against Scheffé's polynomial model for mixture experiments. The notion of model equivalence and the use of various diagnostic measures provide effective tools in making such comparisons, particularly when the experimental region is highly constrained. It is demonstrated that the choice of the best fitting model, through variable selection, depends on which mixture component is selected as a slack variable, and on the size of the fitted model. In addition, the equivalence of two well-known representations of a complete mixture model is shown to be valid. Two numerical examples are presented.     

Leadership in education: Effective U.K. college principals

College leaders face increasing challenges to manage complex responsibilities. They must possess a wide variety of managerial and leadership skills to be successful in academic environments, which are beginning to resemble more traditional organizational frameworks. The study examined in this article explored the relationships among managerial style, organizational climate, and several measures of college‐student performance with a sample of British further education college principals (similar to U.S. community college presidents). While considering the influence of various background factors, such as the size of the college, student funding, and years of experience, several significant relationships emerged demonstrating a significant association between principals' managerial behaviors and measures of college and student performance.     

Social-cognitive determinants of physical activity attendance in older adults

The present study evaluated whether, and to what extent, the constructs implicated in the theory of planned behavior could predict behavioral intention to exercise and exercise-class attendance of older adults (age 65-90 years) already enrolled in a physical activity program. The study also evaluated whether including self-efficacy judgments might improve the predictive capacity of the model. Participants (N = 1,095) were randomly sampled Italian volunteers from exercise classes for older adults. First, they completed questionnaires assessing the above-mentioned constructs. Then, class attendance was recorded during the following 3 months. Results indicated a substantial correspondence between the model and the data. Perceived behavioral control and self-efficacy were the strongest predictors of behavioral intention, whereas attitudes and subjective norms only partially contributed to its prediction. The inclusion of self-efficacy improved the predictive capacity of the overall model. Finally, results showed a weak relation between behavioral intention and attendance rate in physical activity sessions.     

Bank Business Models at Zero Interest Rates

We propose a novel observation-driven finite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and Student’s t distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that units of interest can be classified reliably into distinct components in a variety of settings. In an empirical study of 208 European banks between 2008Q1–2015Q4, we identify six business model components and discuss how their properties evolve over time. Changes in the yield curve predict changes in average business model characteristics.     

Sampling hyperparameters in hierarchical models: Improving on Gibbs for high-dimensional latent fields and large datasets

ABSTRACT

A common Bayesian hierarchical model is where high-dimensional observed data depend on high-dimensional latent variables that, in turn, depend on relatively few hyperparameters. When the full conditional distribution over latent variables has a known form, general MCMC sampling need only be performed on the low-dimensional marginal posterior distribution over hyperparameters. This improves on popular Gibbs sampling that computes over the full space. Sampling the marginal posterior over hyperparameters exhibits good scaling of compute cost with data size, particularly when that distribution depends on a low-dimensional sufficient statistic.     

A review of classic non-asymptotic methods for comparing two proportions by means of independent sampLES

The 2 × 2 tables used to present the data in an experiment for comparing two proportions by means of two observations of two independent binomial distributions may appear simple but are not. The debate about the best method to use is unending, and has divided statisticians into practically irreconcilable groups. In this article, all the available non-asymptotic tests are reviewed (except the Bayesian methodology). The author states which is the optimal (for each group), referring to the tables and programs that exist for them, and contrast the arguments used by supporters of each of the options. They also sort the tangle of solutions into "families", based on the methodology used and/or prior assumptions, and point out the most frequent methodological mistakes committed when comparing the different families.     

David Schmeidler’s contributions to decision theory

Theory and Decision -