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)

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.

Value

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

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

# NOT RUN {
n <- 1000
NMC <- 500
prob <- c(0.99)
# }
# NOT RUN {
 #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)
# }
# NOT RUN {
# }