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An R Package for Univariate and Bivariate Peaks Over Threshold ...

An R Package for Univariate and Bivariate Peaks Over Threshold ...

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3.3 Fitting the GPD3.3.1 The univariate caseThe main function to fit the GPD is called fitgpd. This is a generic function which can fit the GPDaccording several estimators. There are currently 17 estimators available: method of moments moments,maximum likelihood mle, biased <strong>and</strong> unbiased probability weighted moments pwmb, pwmu, mean powerdensity divergence mdpd, median med, pick<strong>and</strong>s’ pick<strong>and</strong>s, maximum penalized likelihood mple <strong>and</strong> maximumgoodness-of-fit mgf estimators. For the mgf estimator, the user has to select which goodness-of-fitstatistics must be used. These statistics are the Kolmogorov-Smirnov, Cramer von Mises, <strong>An</strong>dersonDarling <strong>and</strong> modified <strong>An</strong>derson Darling. See the html help page of the fitgpd function to see all ofthem. Details <strong>for</strong> these estimators can be found in (Coles, 2001), (Hosking <strong>and</strong> Wallis, 1987), (Juárez<strong>and</strong> Schucany, 2004), (Peng <strong>and</strong> Welsh, 2001) <strong>and</strong> (Pick<strong>and</strong>s, 1975).The MLE is a particular case as it is the only one which allows varying threshold. Moreover, two typesof st<strong>and</strong>ard errors are available: “expected” or “observed” in<strong>for</strong>mation of Fisher. The option obs.fishspecifies if we want observed (obs.fish = TRUE) or expected (obs.fish = FALSE).As Pick<strong>and</strong>s’ estimator is not always feasible, user must check the message of feasibility return by functionfitgpd.We give here several didactic examples.> x mom mle pwmu pwmb pick<strong>and</strong>s med mdpd mple ad2r print(rbind(mom, mle, pwmu, pwmb, pick<strong>and</strong>s, med, mdpd, mple,+ ad2r))scale shapemom 1.7563811022 0.21890796mle 1.7669554034 0.21254750pwmu 1.7577270679 0.21830938pwmb 1.7670734558 0.21415289pick<strong>and</strong>s 1.9398703365 0.09973551med 1.9837826885 0.05076009mdpd 1.7779268149 0.20440353mple 1.7825858914 0.20187921ad2r 0.0006977949 47.80717364The MLE, MPLE <strong>and</strong> MGF estimators allow to fix either the scale or the shape parameter. For example,if we want to fit a Exponential distribution, just do (with eventually a fixed scale parameter):> x fitgpd(x, thresh = 1, shape = 0, est = "mle")Estimator: MLEDeviance: 304.1777AIC: 306.1777Varying <strong>Threshold</strong>: FALSE11

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