Search for: All records

The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts. 

Our relationship with technology is constantly evolving, and how we use technology in disasters has evolved even faster. Understanding how to utilize human interactions with technology and the limitations of those interactions will be a crucial building block to contextualizing crisis data. The impact of geographic scale on behavioral change analyses is an unexplored facet of our ability to identify relative severities of crisis situations, magnitudes of localized crises, and total durations of disaster impacts. Within this paper, we aggregate Twitter and hurricane damage data across a wide range of geographic scales and assess the impact of increasing scale on both the recognition of extreme behaviors and the correlation between activity and damage. The power-law relationships identified between many of these variables indicate a direct, definable scalar dependence of social media aggregation analyses, and these relationships can be used to inform more intelligent, equitable, and actionable social media usage in emergency response.