CriticalValue: Computation of the critical value in the hill.adapt function In extremefit: Estimation of Extreme Conditional Quantiles and Probabilities

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

 ```1 2 3``` ```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.

`hill.adapt`
 ```1 2 3 4 5 6 7 8``` ```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) ```