Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas

By Shih-Chieh Kao, Rao S. Govindaraju. 2008

212 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/804047b80a7bba78bde35df0e4012cb9889811a4

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Citations in the corpus

Drought Analysis Using Copulas (2013, background): “To the contrary, metaelliptical copulas, which are extensions of the multivariate Gaussian distribution, can model arbitrary pairwise dependencies between variables though a correlation matrix (Kao and Govindaraju 2008).”
A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall (2011, methodology): “Some useful contributions in this direction are provided by Salvadori and De Michele [2006], Grimaldi and Serinaldi [2006b], Kao and Govindaraju [2008] , Lennartsson et al. [2008], Sun [2008], Bardossy and Pegram [2009], and Serinaldi [2009] and will be the subject of future research.”
A continuous rainfall model based on vine copulas (2015, unknown): “Some applications can be found in the modelling of trivariate rainfall (Zhang and Singh, 2007; Kao and Govindaraju, 2008; Salvadori and De Michele, 2006; Grimaldi and Serinaldi, 2006), trivariate ﬂoods (Serinaldi et al. (2010).”
IDF relationships using bivariate copula for storm events in Peninsular Malaysia (2012, methodology): “The copula approach is a flexible method that allow more choices of marginal distributions and dependence structures to be used in multivariate problems (Kao and Govindaraju, 2008).”
Measure of Correlation between River Flows Using the Copula-Entropy Method (2013, background): “To the contrary, metaelliptical copulas, which are extensions of the multivariate Gaussian distribution, can model arbitrary pairwise dependencies between variables though a correlation matrix (Kao and Govindaraju 2008).”
Entropy–Copula in Hydrology and Climatology (2014, background): “For example, a deﬁcit in precipitation over a certain period of time could lead to a deﬁcit in soil moisture and runoff (Kao and Govindaraju 2008).”
Weighted likelihood copula modeling of extreme rainfall events in Connecticut. (2010, methodology): “As a result, our sample size for annual extreme69 rainfall event is much smaller than that of Kao and Govindaraju (2008), who used hourly70 precipitation to construct rainfall events.”
Copulas and Its Application in Hydrology and Water Resources (2018, background): “The meta-elliptical copulas can provide a wide range of positive and negative degrees of joint behavior and model high-dimensional dependence structure with a very simple structure (Kao and Govindaraju 2008).”
Copula based analysis of rainfall severity and duration: a case study (2013, methodology): “The use of univariate distributions has become the standard approach in rainfall analysis; therefore, despite it being more relevant to climactic related problems, the application of multivariate distributions has not been pursued to any great extent (c.f. Zhang and Singh 2007; Genest and Favre 2007”