extremefit: CriticalValue – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

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.

Examples

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)

## End(Not run)

extremefit

Estimation of Extreme Conditional Quantiles and Probabilities

v1.0.2

GPL-2

Authors

Gilles Durrieu, Ion Grama, Kevin Jaunatre, Quang-Khoai Pham, Jean-Marie Tricot

Initial release

2019-05-03