Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France
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Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...
Despite extensive research on nitrate export and removal, nutrient contamination remains a major threat to water bodies worldwide. At the local scale, nitrate removal is governed by biogeochemical conditions that vary in space and time, making integration ...
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This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV ...
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We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...
The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi α-Divergences and Sibson’s α-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...
Controlling phosphorus is fundamental to limit the risk of eutrophication of continental aquatic ecosystems. Integrated modelling of its concentration in the aquatic continuum requires specific tools for water bodies. However, although simple static empiri ...
Functional time series analysis, whether based on time or frequency domain methodology, has traditionally been carried out under the assumption of complete observation of the constituent series of curves, assumed stationary. Nevertheless, as is often the c ...