Guillaume Évin
Global and Planetary Change · 2008–2018
A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 34 citations into it; #3,652 of 10,225 by trust here, #5,940 by citations
- Water Science and Technology — 16 citations into it
- Finance — 15 citations into it; #6,477 of 10,518 by trust here, #6,671 by citations
- Statistics and Probability — 14 citations into it; #8,279 of 12,932 by trust here, #6,552 by citations
- Artificial Intelligence — 11 citations into it
- Environmental Engineering — 7 citations into it
- Atmospheric Science — 6 citations into it; #3,306 of 4,804 by trust here, #4,676 by citations
Whom Guillaume Évin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thomas Mikosch
- Roger Fletcher
- Christian P. Robert
- Katherine S. Campbell
- Christian Genest
- Rajeshwar Mehrotra
- Dmitri Kavetski
- András Bárdossy
- R. Wojcik
- R. Srikanthan
Who trusts Guillaume Évin overall
- Songbai Song
- Vijay P. Singh
- M. Brunner
- David McInerney
- Reinhard Furrer
- Sander Vandenberghe
- Anne-Catherine Favre
- S. Solari
Papers from Guillaume Évin's perspective
Ordered by how strongly the researchers Guillaume Évin trusts endorse each paper. Same corpus, a different order for every researcher.
- An Introduction to Statistical Modeling of Extreme Values
- Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology
- The future of distributed models: model calibration and uncertainty prediction.
- Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis
- A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors
- Fonctions de repartition a n dimensions et leurs marges
- Estimating the Dimension of a Model
- A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling
- Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors
- A Generalized Pareto intensity‐duration model of storm rainfall exploiting 2‐Copulas
Their papers
- A new rainfall model based on the Neyman‐Scott process using cubic copulas (2008) — 63 citations
- Stochastic generation of multi-site daily precipitation focusing on extreme events (2018) — 100 citations
- Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity (2014) — 156 citations
- Two‐component mixtures of normal, gamma, and Gumbel distributions for hydrological applications (2011) — 57 citations