goftest.hill.adapt: Goodness of fit test statistics

In extremefit: Estimation of Extreme Conditional Quantiles and Probabilities

Goodness of fit test statistics

Description

Give the results of the goodness of fit tests for testing the null hypothesis that the tail is fitted by a Pareto distribution, starting from the adaptive threshold, against the Pareto change point distribution for all possible change points (for more details see pages 447 and 448 of Durrieu et al. (2015)).

Usage

## S3 method for class 'hill.adapt'
goftest(object, plot = FALSE, ...)

Arguments

object	output of the function hill.adapt.
plot	If TRUE, the test statistics are plotted.
...	further arguments passed to or from other methods.

Value

TS.window	the test statistic inside the window. (pages 447 and 448 of Durrieu et al.(2015))
TS	the test statistic.
CritVal	the critical value of the test.

References

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.

See Also

hill.adapt, goftest

Examples

x <- abs(rcauchy(100))
HH <- hill.adapt(x, weights=rep(1, length(x)), initprop = 0.1,
               gridlen = 100 , r1 = 0.25, r2 = 0.05, CritVal=10)

#the critical value 10 is assiociated to the rectangular kernel.

goftest(HH, plot = TRUE)

# we observe that for this data, the null hypothesis that the tail
# is fitted by a Pareto distribution is not rejected as the maximal
# value in the graph does not exceed the critical value.

extremefit documentation built on April 11, 2025, 6:10 p.m.