Generalized maximum likelihood estimators for the nonstationary generalized extreme value model

By S. El Adlouni, Taha B. M. J. Ouarda, Xuebin Zhang, R. Roy, Bernard Bobée. 2007

425 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/daff8050778b4213077a83898076f531471993a0

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Citations in the corpus

The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM(ABC) (2015, background): “…Cunderlik and Ouarda, 2006; Leclerc and Ouarda, 2007; Dettinger, 2011; Stedinger and Griffis, 2011; Chebana et al., 2013], and the use of generalized extreme value (GEV) and max-stable models [Clarke, 2002; El-Adlouni et al., 2007; Westra and Sisson, 2011; Nasri et al., 2013; Westra et al., 2013].”
Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo (2009, methodology): “More recently, the generalized maximum likelihood (GML) method, developed originally for the classical model (GEV), was extended to the case with covariates [El Adlouni et al., 2007].”
Non-Stationary Frequency Analysis of Extreme Water Level: Application of Annual Maximum Series and Peak-over Threshold Approaches (2017, methodology): “The non-stationary GEV model, for example, is an efficient tool to incorporate the dependencies between the extreme value data (El Adlouni et al. 2007; Cannon 2010).”
Spatial regression models for extreme precipitation in Belgium (2012, methodology): “Variations in time can be modeled by introducing time-dependent covariates in the GEV parameters [Smith, 1989; Coles, 2001; Katz et al., 2002; El Adlouni et al., 2007].”
Non‐stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil (2009, background): “…becomes questionable, and often statistical modelling of EV needs to be properly modified to accommodate, for instance, a time-trend component, either linear or non-linear (e.g. Strupczewski et al., 2001a,b; Katz et al., 2002, 2005; Khaliq et al., 2006; El Adlouni et al., 2007; Nogaj et al., 2007).”
Bayesian Nonstationary Frequency Analysis of Hydrological Variables 1 (2011, methodology): “Aside from using the information provided by the data sample, the Bayesian framework allows one to formally incorporate other ‘‘prior’’ sources of information that may be available concerning the parameters of interest (El-Adlouni et al., 2007).”
Non‐stationary intensity‐duration‐frequency curves integrating information concerning teleconnections and climate change (2018, methodology): “El Adlouni et al. (2007) developed a method for the estimation of probability distribution parameters involving covariates.”
Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula (2015, background): “, 2006; El Adlouni et al., 2007; Gilroy and McCuen, 2012). As done by Villarini et al. (2009a), a total of four different marginal distribution models can be considered in the nonstationarity analysis: (1) all distribution parameters are constant, (2) only the location parameter is time varying,”