goftest.hill.ts {extremefit} | R Documentation |
Give the results of the goodness of fit test for testing the null hypothesis that the tail is fitted by a Pareto distribution starting from the adaptive threshold (for more details see pages 447 and 448 of Durrieu et al. (2015)).
## S3 method for class 'hill.ts' goftest(object, X, t, plot = FALSE, ...)
object |
output of the hill.ts function. |
X |
a vector of the observed values. |
t |
a vector of time covariates which should have the same length as X. |
plot |
If |
... |
further arguments passed to or from other methods. |
TS.window |
the maximum value of test statistics inside the window for each t in Tgrid (see help(hill.ts) ). |
TS.max |
the maximum value of test statistics for each t in Tgrid (see help(hill.ts) ). |
CritVal |
the critical value of the test. |
Grama, I. and Spokoiny, V. (2008). Statistics of extremes by oracle estimation. Ann. of Statist., 36, 1619-1648.
Durrieu, G. and Grama, I. and Pham, Q. and Tricot, J.- M (2015). Nonparametric adaptive estimator of extreme conditional tail probabilities quantiles. Extremes, 18, 437-478.
theta<-function(t){0.5+0.25*sin(2*pi*t)} n<-5000 t<-1:n/n Theta<-theta(t) Data<-NULL Tgrid<-seq(0.01,0.99,0.01) #example with fixed bandwidth for(i in 1:n){Data[i]<-rparetomix(1,a=1/Theta[i],b=5/Theta[i]+5,c=0.75,precision=10^(-5))} ## Not run: #For computing time purpose #example hgrid <- bandwidth.grid(0.009, 0.2, 20, type = "geometric") TgridCV <- seq(0.01, 0.99, 0.1) hcv <- bandwidth.CV(Data, t, TgridCV, hgrid, pcv = 0.99, TruncGauss.kernel, kpar = c(sigma = 1), CritVal = 3.6, plot = TRUE) Tgrid <- seq(0.01,0.99,0.01) hillTs <- hill.ts(Data, t, Tgrid, h = hcv$h.cv, TruncGauss.kernel, kpar = c(sigma = 1), CritVal = 3.6, gridlen = 100, initprop = 1/10, r1 = 1/4, r2 = 1/20) goftest(hillTs, Data, t, plot = TRUE) # we observe that for this data, the null hypothesis that the tail # is fitted by a Pareto distribution is not rejected # for all points on the Tgrid ## End(Not run)