extremefit (version 1.0.2)

CriticalValue: Computation of the critical value in the hill.adapt function

Description

For a given kernel function, compute the critical value (CritVal) of the test statistic in the hill.adapt function by Monte-Carlo simulations.

Usage

CriticalValue(NMC, n, kernel = TruncGauss.kernel, kpar = NULL,
  prob = 0.95, gridlen = 100, initprop = 0.1, r1 = 0.25,
  r2 = 0.05, plot = FALSE)

Value

For the type 1 errors \(prob\), this function returns the critical values.

Arguments

NMC

the number of Monte-Carlo simulations.

n

the sample size.

kernel

a kernel function for which the critical value is computed. The available kernel functions are Epanechnikov, Triangular, Truncated Gaussian, Biweight and Rectangular. The truncated gaussian kernel is by default.

kpar

a value for the kernel function parameter, with no default value.

prob

a vector of type 1 errors.

gridlen, initprop, r1, r2

parameters used in the function hill.adapt (see hill.adapt).

plot

If TRUE, the empirical cummulative distribution function and the critical values are plotted.

References

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

Examples

n <- 1000
NMC <- 500
prob <- c(0.99)
if (FALSE)  #For computing time purpose
  CriticalValue(NMC, n, TruncGauss.kernel, kpar = c(sigma = 1), prob, gridlen = 100 ,
                initprop = 1/10, r1 = 1/4, r2 = 1/20, plot = TRUE)