A Bayesian hierarchical approach to regional frequency analysis
By Benjamin Renard. 2011
123 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/d619ce2283ec41d002033a1e2a93c2c3cdab0210
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
- C. Bracken
- Upmanu Lall
- Zengchao Hao
- Vijay P. Singh
- K. Holman
- Hamid Moradkhani
- Balaji Rajagopalan
- C. Lima
In Ocean Engineering
- C. Bracken
- Upmanu Lall
- Zengchao Hao
- Vijay P. Singh
- K. Holman
- Hamid Moradkhani
- Balaji Rajagopalan
- C. Lima
Citations in the corpus
- Entropy‐copula method for single‐site monthly streamflow simulation (2012, background): “One of the most common applications is frequency analysis of hydrological variables [Favre et al., 2004; Salvadori and De Michele, 2004; Genest et al., 2007; Kao and Govindaraju, 2008; Chebana and Ouarda, 2009; Salvadori and De Michele, 2010; Renard, 2011; Vandenberghe et al., 2011].”
- A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates (2016, methodology): “L-moments, generalized maximum likelihood, Bayesian methods, etc) has been also subject of much research (Katz et al., 2002; Kwon et al., 2008; Madsen and Rosbjerg, 1997; Parent and Bernier, 2003; Martins and Stedinger, 2000; Rao and Hamed, 2000; Renard, 2011; Renard et al., 2013; Smith, 1989; Smith”
- A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “Because of their ability to represent any marginal distribution, elliptical copulas have become a popular practical tool for modeling multivariate extremes (Ghosh & Mallick, 2011; Renard, 2011; Renard & Lang, 2007; Sang & Gelfand, 2010).”
- Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, background): “…trade-off between the flexibility of the model and the level of complexity that can be identified from the data is a very challenging task (Renard, 2011), because the Box-Cox algorithm can transform nonnormally distributed data to a set of data that are reasonably close to a normal…”