A review of bivariate gamma distributions for hydrological application

By S. Yue, Taha B. M. J. Ouarda, Bernard Bobée. 2001

238 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cbb13cb02690cfbbcadded3dfd8535e2568a68aa

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Citations in the corpus

Probabilistic assessment of flood risks using trivariate copulas (2012, background): “1999; Yue 2001a; Nadarajah and Shiau 2005; Escalante 2007), bivariate gamma distribution (Yue 2001b; Yue et al. 2001), bivariate normal distribution (Goel et al.”
Multivariate drought characteristics using trivariate Gaussian and Student t copulas (2013, methodology): “Application of copula technique can successfully overcome/avoid the difficulties encountered in conventional bivariate models and drought analyses (Singh and Singh, 1991; Bacchi et al., 1994; Frees and Valdez, 1998; Goel et al., 1998; Yue, 1999; Yue et al., 1999, 2001; Shiau, 2003).”
Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate (2018, methodology): “, Gaussian, exponential and gamma types) (Singh and Singh, 1991; Goel et al., 1998; Yue et al., 2001), kernel density estimators (Kim et al.”
Bootstrap Pettitt test for detecting change points in hydroclimatological data: case study of Itaipu Hydroelectric Plant, Brazil (2019, background): “The gamma distribution has the advantage of presenting only positive values, as occur in some hydrological series such as precipitation and flow, and has good applicability in these cases (Aksoy 2000, Yue et al. 2001, Yoo et al. 2005).”
Multivariate L‐moment homogeneity test (2007, background): “[3] A thorough understanding of multivariate hydrological events requires the study of the joint probabilistic behavior of two or more correlated random variables that characterize the events [Yue et al., 2001].”
A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean (2016, background): “rather than gamma distribution we found that the performance was different, similar to what was found by Yue et al. (2002) for the Mann-Kendall and”
IDF relationships using bivariate copula for storm events in Peninsular Malaysia (2012, background): “For instance, the marginals are assumed to be either normal (Yue, 2000) or possess the same type of probability distribution such as bivariate exponential (Favre et al., 2002; Goel et al., 2000), bivariate Gamma (Yue et al., 2001), bivariate lognormal (Yue, 2002) and bivariate extreme value distribu”
Multivariate hydrological frequency analysis using copulas (2004, methodology): “This distribution is frequently used in hydrology for volumes [Yue et al., 2001].”
Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, methodology): “…focused on bivariate normal distribution to perform flood analysis with later researchers considering multivariate Gaussian (Krstanovic and Singh, 1987), gamma (Yue et al., 2001; Nadarajah and Gupta, 2006), exponential (Choulakian et al., 1990), Gumbel (Bacchi et al., 1994) and other distributions.”
Multivariate non-normally distributed random variables in climate research - introduction to the copula approach (2008, methodology): “the FarlieGumbel-Morgenstern model we refer to a review of bivariate gamma distributions (Yue et al., 2001).”