Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.     

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.     

The shape of mortality curves: an analysis of counties in England and Wales, 1911

Previous analysis has shown that life tables may be distinguished by two orthogonal pieces of information, the level of mortality in the population and the relative shape of the mortality curve. We show that both the regions of England and Wales in 1911, and locality types (county boroughs, other urban, and rural) differ in the shape of their mortality curves. Using data for the administrative counties, we examine the underlying correlates of this differentiation, paying particular attention to the socio-economic structure of the counties as reflected in their wealth, opportunity levels, and degree of urbanisation.     

Why social status matters for understanding the interrelationships between testosterone,economic risk‐taking,and gender

We conduct an extensive review of the literature on testosterone and economic risk‐taking behavior. In sum, there is evidence of a positive association between testosterone and economic risk taking, although it is unlikely to be a strong association given the abundance of null results. However, we argue that the existing literature may overstate the causal effects of testosterone on economic risk taking (or even report a spurious correlation) because this research has not considered the potentially confounding role of social status. Status could concurrently influence both testosterone and economic risk taking, given that testosterone is a social hormone with a reciprocal relationship with social status, and social status has been found to drive risk‐taking behavior. We also argue against using findings from this literature to make gender essentialist claims, primarily because social phenomena influence the size—and existence—of gender differences in economic risk‐taking behavior. We conclude with suggestions for future research.     

Snake parasitism in an urban old-growth forest

Urban-associated changes can have immediate or long-term consequences on animal populations. Such changes may be assessed through parasite prevalence and abundance in wildlife hosts, as urbanization can influence parasitism and disease transmission in wildlife. Snakes are widespread and diverse vertebrates that often persist in urban environments; however, parasitism of snakes in urban environments has yet to be studied, leaving the roles of snakes in parasite transmission uncharacterized. Field ecology, microscopy, molecular techniques, and geographic information science (GIS) were integrated to characterize parasitism of snakes in an urban old-growth forest park. The species, sex, mass, length, location, and prevalence of ecto-, hemo-, and fecal parasites were determined for 34 snakes of 6 species. Ectoparasites (mite), hemoparasites (Hepatozoon spp.), and fecal parasites (Entamoeba spp., Trichomonas spp., Strongloides spp., and an unidentified helminth) were detected in snakes and 64.7?% of snakes were infected by at least one of these parasites. Parasite infections were generally not related to the sex, age, or body condition of snakes. The locations of infected snakes were used to produce risk maps indicating where parasite prevalence is predicted to be greatest. The analysis of these maps indicated that snakes with fecal parasites were closer than non-infected snakes to the edge of the forest. This study confirms that snakes may be important parasite hosts or reservoirs in parasite transmission pathways in urban environments and it provides an integrative multidisciplinary approach that may be used to monitor parasitism dynamics in other urban wildlife areas.     

Never easy to say “sorry”: Exploring the interplay of crisis involvement,brand image,and message appeal in developing effective corporate apologies

This study examines how the interplay among crisis involvement, brand image, and message appeal impacts the effectiveness of an apology in the context of a corporate crisis. To determine apology effectiveness, a 2 (crisis involvement: high vs. low) × 2 (brand image: symbolic vs. functional) × 2 (message appeal type: emotional vs. informational) experimental design was employed. The results demonstrate that the three-way interaction is significant, especially in the low-crisis involvement condition. The study concludes with a discussion of theoretical and practical implications and directions for future research.