A fully probabilistic approach to extreme rainfall modeling

By Stuart Coles, Luis Raúl Pericchi, Scott Anthony Sisson. 2003

313 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/260a38775915a4a67978f1fb9ccc1b3983c903ca

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Citations in the corpus

Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation / Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: I. Recherche théorique (2004, background): “Other studies (Wilks, 1993; Koutsoyiannis & Baloutsos, 2000; Coles et al. 2003; Coles & Pericchi, 2003; Sisson et al., 2003) have extended the scepticism for the Gumbel distribution to the case of rainfall extremes, showing that it underestimates seriously the largest extreme rainfall amounts.”
Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels (2010, background): “Such seasonal approaches have already been considered in Bayesian models (Coles and Pericchi 2003; Coles et al. 2003).”
Bayesian MCMC flood frequency analysis with historical information (2005, background): “Moreover, a Bayesian analysis can provide credible intervals for parameters or any function of the parameters which are more easily interpreted than the concept of confidence interval in classical statistics (Congdon, 2001). Kuczera (1996) shows that correlated measurement errors from extrapolation ”
Time-varying models for extreme values (2007, background): “Coles et al. (2003) present an analysis of these data, which are very relevant due to the catastrophic events that occurred in the northern coast of Venezuela in December 1999.”
An application of Bayesian analysis and Markov chain Monte Carlo methods to the estimation of a regional trend in annual maxima (2006, unknown): “…1999; Perreault et al., 1999, 2000a, 2000b, 2000c; Thyer and Kuczera, 2000, 2003a, 2003b; Wang, 2001; Thyer et al., 2002; Coles and Pericchi, 2003; Coles et al., 2003; Parent and Bernier, 2003; Perreault and Fortin, 2003; Tapsoba et al., 2004; Moyeed and Clarke, 2005; Reis and Stedinger, 2005;…”
ExtRemes 2.0: An extreme value analysis package in R (2016, background): “The latter approach does not require fitting 4Based on practical considerations as in Coles, Pericchi, and Sisson (2003) and on penultimate approximations in extreme value theory (Reiss and Thomas 2007). the Gumbel df to the data.”
Sensitivity of goodness-of-fit statistics to rainfall data rounding off (2006, methodology): “This approach was followed for instance by De Michele and Salvadori (2005). 3.”
Model and parameter uncertainty in IDF relationships under climate change (2015, methodology): “Bayesian methods are proven to be the most desirable way of analyzing the sparse data, waiving the concerns of repeated sampling used by classical methods [9,21].”
Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach (2016, methodology): “Bayesian analysis has been used previously for, amongst others, extreme precipitation modelling (e.g. Coles and Tawn 1996, Coles 2001, Coles et al. 2003, Smith 2005, Alston 2011, Eli et al. 2012, Sun et al. 2015).”
A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series (2010, background): “…least reason, there is much published evidence of the good capability of the GPD in describing rainfall exceedances (see e.g.,Cameron et al., 2000; Coles et al., 2003; De Michele and Salvadori, 2005; Fitzgerald, 1989; Madsen et al., 2002; Salvadori and De Michele, 2001; Van Montfort and Witter,…”
Anticipating catastrophes through extreme value modelling (2003, background): “More credible results can be obtained by using confidence intervals based on standard asymptotic approximations of appropriate profile likelihood functions (Coles et al., 2003), but the absence of a probabilistic formalism for design values obtained this way remains troublesome.”
Marginal methods of intensity‐duration‐frequency estimation in scaling and nonscaling rainfall (2007, methodology): “[4] The drawbacks of the annual maximum methods are also well known [Coles and Tawn, 1996; Coles et al., 2003]: these methods make poor use of the data when continuous records are available, lack robustness when extrapolating to high return periods, and fit extreme value distributions to data that a”
Historical changes and future projection of extreme precipitation in China (2015, background): “The generalized extreme value (GEV) distribution is widely applied for modeling extreme events in meteorology and many other fields (Coles et al. 2003; Khaliq et al. 2006).”
Threshold detection for the generalized Pareto distribution: Review of representative methods and application to the NOAA NCDC daily rainfall database (2016, background): “This makes the estimated distribution parameters to exhibit significant variability and be sensitive to outliers [see e.g., Coles and Tawn, 1996; Coles et al., 2003; Koutsoyiannis, 2004b; Deidda, 2010; Langousis et al., 2013].”