CriticalValue {extremefit} R Documentation

## 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.

`hill.adapt`

### 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)

```

[Package extremefit version 1.0.2 Index]