Results 81 to 90 of about 292 (177)

Return Period of Nonconcurrent Climate Compound Events: A Nonparametric Bivariate Generalized Pareto Approach

Environmetrics, Volume 37, Issue 1, January 2026.
ABSTRACT Compound events (CEs), commonly defined as the “combination of multiple drivers and/or hazards that contributes to societal or environmental risk”, often result in amplified impacts compared to individual hazards. In order to estimate the return period of bivariate CEs, a novel nonparametric approach employing bivariate Generalized Pareto ...
Grégoire Jacquemin, Denis Allard, Xavier Freulon, Mathieu Vrac   +3 more
wiley   +1 more source

Spatial Analysis of Agricultural Waste and By‐Products to Tackle the Water–Energy Nexus in Rural Mozambique

Global Challenges, Volume 10, Issue 1, January 2026.
This study presents a geographic information system‐based, participatory methodology to identify high‐potential zones for agricultural waste valorization in rural Mozambique. By integrating spatial data with local knowledge, it reveals opportunities for bioenergy production and sustainable water use. The findings support circular development strategies,
Giuseppe Mancuso, Valentina Morini, Gonzalo A. Martinez, Attilio Toscano, Francesca Valenti   +4 more
wiley   +1 more source

Simplified vine copula models: State of science and affairs

Risk Sciences
Vine copula models have become highly popular practical tools for modeling multivariate dependencies. To maintain tractability, a commonly employed simplifying assumption is that conditional copulas remain unchanged by the conditioning variables.
Thomas Nagler
doaj   +1 more source

Operational Convection‐Permitting COSMO/ICON Ensemble Predictions at Observation Sites (CIENS)

Geoscience Data Journal, Volume 13, Issue 1, January 2026.
Map of synoptic stations in Germany for which ensemble forecasts and observations are provided in the CIENS dataset. Colours represent the station altitude in metres. ABSTRACT We present the CIENS dataset, which contains ensemble weather forecasts from the operational convection‐permitting numerical weather prediction model of the German Weather ...
Sebastian Lerch, Benedikt Schulz, Reinhold Hess, Annette Möller, Cristina Primo, Sebastian Trepte, Susanne Theis   +6 more
wiley   +1 more source

Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2015
The article points out the possibilities of using static D-Vine copula ARMA-GARCH model for estimation of 1 day ahead market Value at Risk. For the illustration we use data of the four companies listed on Prague Stock Exchange in range from 2010 to 2014.
Václav Klepáč, David Hampel
doaj   +1 more source

Learning Vine Copula Models for Synthetic Data Generation

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development.
Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni   +2 more
openaire   +3 more sources

Pruning and Truncating the Mixture R-Vine Model Using the Mixture Weight

Journal of Probability and Statistics
Vine copula mixture models are highly flexible and can handle complex hidden dependencies among variables without restricting the parametric shape of the margins or the type of dependency structure.
Fadhah Alanazi
doaj   +1 more source

Contributions to Vine-Copula Modeling

, 2022
Regular vine-copula models (R-vines) are a powerful statistical tool for modeling thedependence structure of multivariate distribution functions. In particular, they allow modelingdierent types of dependencies among random variables independently of their marginaldistributions, which is deemed the most valued characteristic of these models.
openaire   +1 more source

Truncation Mixture R-Vine Copulas

, 2021
Uncovering hidden mixture correlation among variables have been investigating in the literature using mixture R-vine copula models. These models are hierarchical in nature. They provides a huge flexibility for modelling multivariate data. As the dimensions increases, the number of the model parameters that need to be estimated is increased dramatically,
openaire   +2 more sources

Copula Modeling of COVID-19 Excess Mortality

Risks
COVID-19’s effects on mortality are hard to quantify. Issues with attribution can cause problems with resulting conclusions. Analyzing excess mortality addresses this concern and allows for the analysis of broader effects of the pandemic.
Jonas Asplund, Arkady Shemyakin
doaj   +1 more source