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.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

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.     

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.     

Improving performance? The adoption and implementation of quality systems in U.K. nonprofits

There are increasing and multiple pressures on nonprofit organizations to demonstrate excellence in performance. Although there is a growing literature on the various approaches to performance improvement taken by nonprofits, little is known about the processes involved in the adoption and implementation of specific approaches. This article is about the adoption and use of one approach to performance improvement, “quality systems,” in the U.K. nonprofit sector. We report findings about factors that encourage nonprofits to adopt quality systems. We also analyze the distinctive challenges of implementing quality approaches in a nonprofit sector context and suggest critical success factors. The article concludes with a discussion of the organizational and policy implications of applying the management concepts of quality and performance to the nonprofit sector.     

Class analysis and its futures

Books reviewed in this articles:
Class and Stratification: An introduction to current debates R. Crompton
Class S. Edgell
Changing Classes: Stratification and Mobility in Post-Industrial Societies G. Esping-Anderson, (ed.)
Class and Inequality: Comparative Perspectives M. Hamilton and M. Hirszowicz
Class R. Scase