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Heavy rainfall distributional modeling is essential in any impact studies linked to the water cycle, for example, flood risks. Still, statistical analyses that both take into account the temporal and multivariate nature of extreme rainfall are rare, and often, a complex de-clustering step is needed to make extreme rainfall temporally independent. A natural question is how to bypass this de-clustering in a multivariate context. To address this issue, we introduce the stable sums method. Our goal is to incorporate time and space extreme dependencies in the analysis of heavy tails. To reach our goal, we build on large deviations of regularly varying stationary time series. Numerical experiments demonstrate that our novel approach enhances return levels inference in two ways. First, it is robust concerning time dependencies. We implement it alike on independent and dependent observations. In the univariate setting, it improves the accuracy of confidence intervals compared to the main estimators requiring temporal de-clustering. Second, it thoughtfully integrates the spatial dependencies. In simulation, the multivariate stable sums method has a smaller mean squared error than its component-wise implementation. We apply our method to infer high return levels of daily fall precipitation amounts from a national network of weather stations in France.  相似文献   
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Recent studies on ecological networks have quantified the contribution of ecological, historical, and evolutionary factors on the structure of local communities of interacting species. However, the influence of species’ biogeographical traits, such as migratory habits or phylogeographical history, on ecological networks is poorly understood. Meta-networks, i.e., networks that cover large spatial extensions and include species not co-occurring locally, enable us to investigate mechanisms that operate at larger spatial scales such as migratory patterns or phylogeographical distributions, as well as indirect relationships among species through shared partners. Using a meta-network of hummingbird-plant interaction across Mexico, we illustrate the usefulness of this approach by investigating (1) how biogeographical and morphological factors associate with observed interactions and (2) how species-specific biogeographical characteristics associate with species’ network roles. Our results show that all studied hummingbird and plant species in the meta-network were interrelated, either directly or through shared partners. The meta-network was structured into modules, resulting from hummingbirds and plants interacting preferentially with subsets of species, which differed in biogeographical, and, to a lesser extent, morphological traits. Furthermore, migrants and hummingbirds from Nearctic, Transition, and widespread regions had a higher topological importance in the meta-network. Our study illustrates how meta-networks may contribute to our current knowledge on species’ biogeographical traits and biotic interactions, providing a perspective complementary to local-scale networks.  相似文献   
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The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.  相似文献   
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