Computation of Sharp Bounds on the Expected Value of a Supermodular Function of Risks with Given Marginals

We show that the rearrangement algorithm (RA) introduced in Puccetti and Rüschendorf (2012 Puccetti, G., Rüschendorf, L. (2012). Computation of sharp bounds on the distribution of a function of dependent risks. Journal of Computational and Applied Mathematics 236(7):1833–1840.[Crossref], [Web of Science ®] , [Google Scholar]) to compute distributional bounds can be used also to compute sharp lower and upper bounds on the expected value of a supermodular function of d random variables having fixed marginal distributions. Compared to the analytical methods existing in the literature the algorithm is widely applicable, more easily obtained and gives insight into the dependence structures attaining the bounds.     

Regression Methods for Combining Multiple Classifiers

As no single classification method outperforms other classification methods under all circumstances, decision-makers may solve a classification problem using several classification methods and examine their performance for classification purposes in the learning set. Based on this performance, better classification methods might be adopted and poor methods might be avoided. However, which single classification method is the best to predict the classification of new observations is still not clear, especially when some methods offer similar classification performance in the learning set. In this article we present various regression and classical methods, which combine several classification methods to predict the classification of new observations. The quality of the combined classifiers is examined on some real data. Nonparametric regression is the best method of combining classifiers.     

Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure

ABSTRACT

Social media data are increasingly used by researchers to gain insights on individuals’ behaviors and opinions. Platforms like Twitter provide access to individuals’ postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents’ Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user’s friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents’ tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.     

Corporate political power and US foreign policy, 1981–2002: the role of the policy-planning network

Theory and Society - Recent empirical work has offered strong support for ‘biased pluralism’ and ‘economic elite’ accounts of political power in the United States, according...     

Inequality in Volunteering: Building a New Research Front

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Volunteering research focuses predominantly on predicting participation in volunteering, proceeding from the...     

Climate events and the role of adaptive capacity for (im-)mobility

Population and Environment - The study examines the relationship between sudden- and gradual-onset climate events and migration, hypothesizing that this relationship is mediated by the adaptive...     

Aging and Gender Health Gap: A Multilevel Analysis for 17 European Countries

Social Indicators Research - The European population is aging and their declining capacity makes older Europeans more dependent on the availability of care. Male and female health needs at older...