People with disability and new disaster communications: access and the social media mash-up

This article explores how a lack of access to increasingly complex and overlapping digital communications platforms in times of disaster for people with disabilities has the potential to make already life-threatening situations considerably more dangerous. As we are increasingly coming to rely on a social media mash-up of digital platforms to assist in communications during disaster situations, the issue of accessibility for people with disabilities is as dire as if it was high ground during a tsunami or transport during a typhoon. The contemporary social media environment is characterised by a complex and overlapping network of complementary platforms, populated by user-generated content, where people communicate and exchange ideas. In this environment, YouTube videos are posted to Facebook and embedded in blogs, and Twitter is used to link to these other sites and is itself embedded in other platforms. These networks are increasingly supplementing and supplanting more traditional communication platforms, such as the television and radio, particularly in times of disaster. The concern of this paper is that the elements from which this mash-up of communications channels is made are not always accessible to people with disabilities. This evolving network of social media-based communication exposes the limits of existing Internet-based universal design.     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

Small Town in Mass Society Revisited1

Abstract The case study of a small New York town that dramatized the thesis that the secular expansion of macro forces—urbanization, industrialization, bureaucratization—has permanently reduced the autonomy of all small communities is an example of a special type of discovery/persuasion strategy in the social sciences: the “opposition case study.” In contrast to the more rigorous “competitive test” or the atheoretical “negative case,” opposition case studies confront the dominant perspective with a qualitative illustration of a new theory in the context of a zero-sum game. When they are successful, opposition cases meet four criteria: the dominant view is immediately rendered obsolete; the origin of the new idea supports its plausibility; the new perspective is shown to be testable; and the new perspective quickly generates new lines of research. Small Town in Mass Society meets the first criterion, and may have been heuristic, but its probable origin in populist ideology undermines its testability.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.