extremis (version 1.2.1)

cdensity: Kernel Smoothed Scedasis Density

Description

This function computes a kernel scedasis density estimate.

Usage

cdensity(Y, threshold = quantile(Y[, 2], 0.95), ...)

Value

c

scedasis density estimator.

k

number of exceedances above the threshold.

w

standardized indices of exceedances.

Y

raw data.

The plot method depicts the smooth scedasis density.

Arguments

Y

data frame from which the estimate is to be computed; first column corresponds to time and the second to the variable of interest.

threshold

value used to threshold the data y; by default threshold = quantile(y, 0.95).

...

further arguments for density methods.

Author

Miguel de Carvalho

Details

Kernel smoothing for the scedasis density was introduced by Einmahl et al (2016).

References

Einmahl, J. H., Haan, L., and Zhou, C. (2016) Statistics of heteroscedastic extremes. Journal of the Royal Statistical Society: Ser. B, 78(1), 31--51.

Examples

data(lse)
attach(lse)
Y <- data.frame(DATE[-1], -diff(log(ROYAL.DUTCH.SHELL.B)))
T <- dim(Y)[1]
k <- floor((0.4258597) * T / (log(T)))
fit <- cdensity(Y, kernel = "biweight", bw = 0.1 / sqrt(7), 
                threshold = sort(Y[, 2])[T - k])
plot(fit)
plot(fit, original = FALSE)