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.     

Some new constructions of circular balanced repeated measurements designs

Repeated measurements designs are widely used in medicine, pharmacology, animal sciences, and psychology. In this article, some infinite series are developed to generate the balanced repeated measurements designs for p (periods) even. For p odd, construction procedures are also described. Catalogues of the proposed designs are also presented for p = 5, 7, 9, when v ≤ 100.     

Sample size re‐estimation incorporating prior information on a nuisance parameter

Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter–based sample size re‐estimation in a design with an internal pilot study. We focus on trials with continuous endpoints in which the outcome variance is the nuisance parameter. For planning and analyzing the trial, frequentist methods are considered. Moreover, the external information on the variance is summarized by the Bayesian meta‐analytic‐predictive approach. To incorporate external information into the sample size re‐estimation, we propose to update the meta‐analytic‐predictive prior based on the results of the internal pilot study and to re‐estimate the sample size using an estimator from the posterior. By means of a simulation study, we compare the operating characteristics such as power and sample size distribution of the proposed procedure with the traditional sample size re‐estimation approach that uses the pooled variance estimator. The simulation study shows that, if no prior‐data conflict is present, incorporating external information into the sample size re‐estimation improves the operating characteristics compared to the traditional approach. In the case of a prior‐data conflict, that is, when the variance of the ongoing clinical trial is unequal to the prior location, the performance of the traditional sample size re‐estimation procedure is in general superior, even when the prior information is robustified. When considering to include prior information in sample size re‐estimation, the potential gains should be balanced against the risks.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Social mobility and demand for redistribution in Europe: a comparative analysis

The literature on preferences for redistribution has paid little attention to the effect of social mobility on the demand for redistribution and no systematic test of the hypotheses connecting social mobility and preferences for redistribution has yet been done to date. We use the diagonal reference model to estimate the effect of origin and destination classes on preferences for redistribution in a large sample of European countries using data from the European Social Survey. Our findings are consistent with the logic of acculturation in the sense that newcomers tend to adapt their views to those of the destination class at early stages and that upward and downward mobility do not have distinctive effects on the formation of political preferences. However, even though social origins seem to have a limited impact on preferences for redistribution, the evidence does not support the hypothesis that mobile and non‐mobile individuals are alike. We also find that the effect of social origin on preferences varies largely across countries. The empirical evidence leads to the conclusion that the effect of social origin on preferences for redistribution increases in contexts of strong familism.     

Your lying,incompetent and selfish member of Congress: Cynical appeals in U.S. Senate advertising

Scholars still do not fully understand what activates cynicism in citizens. Although many expect that negative campaigning contributes to this, no consistent evidence has been found. This research introduces a new measure of appeals to cynicism that expands the commonly used positive–negative taxonomy of tone in advertising. Through a content analyses of more than 600 political advertisements aired during U.S. Senate elections, we identify the extent to which candidates use cynical appeals and the conditions under which these are used. We find that appeals to cynicism are common, are distinct from negative appeals, and that most often, ad sponsors target opponents with cynical appeals, describing them as selfish, dishonest and incompetent. Cynicism appears to be a central part of American campaign rhetoric, used by all sorts of candidates, regardless of party, in many different situations. The prevalence of appeals to cynicism may help explain rising cynicism among the public.     

Strong convergence rates of probabilistic integrators for ordinary differential equations

Probabilistic integration of a continuous dynamical system is a way of systematically introducing discretisation error, at scales no larger than errors introduced by standard numerical discretisation, in order to enable thorough exploration of possible responses of the system to inputs. It is thus a potentially useful approach in a number of applications such as forward uncertainty quantification, inverse problems, and data assimilation. We extend the convergence analysis of probabilistic integrators for deterministic ordinary differential equations, as proposed by Conrad et al. (Stat Comput 27(4):1065–1082, 2017. https://doi.org/10.1007/s11222-016-9671-0), to establish mean-square convergence in the uniform norm on discrete- or continuous-time solutions under relaxed regularity assumptions on the driving vector fields and their induced flows. Specifically, we show that randomised high-order integrators for globally Lipschitz flows and randomised Euler integrators for dissipative vector fields with polynomially bounded local Lipschitz constants all have the same mean-square convergence rate as their deterministic counterparts, provided that the variance of the integration noise is not of higher order than the corresponding deterministic integrator. These and similar results are proven for probabilistic integrators where the random perturbations may be state-dependent, non-Gaussian, or non-centred random variables.

     

The racialized patterns of police violence: The critical importance of research as praxis

The tragic killing of George Floyd at the hands of the police resulted in hundreds of thousands of protestors marching in the streets demanding change. The call for change criticized the killing of Blacks by law enforcement and challenged White supremacy as an institution of social control and racial violence. A key component of the marches and protests was a message to the residents of the United States: “Black Lives Matter.” As society grapples with a reckoning, researchers studying police violence for the past 6 decades have been empirically and theoretically debating the reasons why use of force by law enforcement continues to have a higher proportion of Black and Brown victims compared to Whites. Although the research on fatal police killings was studied by only a small number of individuals prior to 2014, after the killing of Michael Brown in Ferguson, Missouri the research in different areas of the country increased rapidly as did the forms of analysis and research disciplines making their own contributions. The Washington Post and Guardian news agencies established that deaths at the hands of the police were occurring for at least 900 to 1100 individual deaths per year for which firearms resulted in the greatest cause of death. As US government agencies failed to produce a national data source on police involved killings, the media took a leading role in providing greater national understanding. The authors examine what role academic researchers contribute to the discussion for solutions, particularly those from marginalized backgrounds. As protestors march, lawyers sue and defend, and politicians create new forms of legislation, researchers need to play a more important role initiating critical studies, making sense of the data, and providing a theoretical framework for which police violence can be understood. This article will provide an overview of the literature on racialized police violence, point out key patterns involving racial and ethnic disparities, and emphasize how researchers can play a more important role in advocating for change.