pick_est: Local Pickands' frontier estimator

In npbr: Nonparametric Boundary Regression

Local Pickands' frontier estimator

Description

Computes the Pickands type of estimator introduced by Gijbels and Peng (2000).

Usage

pick_est(xtab, ytab, x, h, k, type="one-stage")

Arguments

xtab	a numeric vector containing the observed inputs x_1,\ldots,x_n.
ytab	a numeric vector of the same length as xtab containing the observed outputs y_1,\ldots,y_n.
x	a numeric vector of evaluation points in which the estimator is to be computed.
h	determines the bandwidth at which the estimate will be computed.
k	a numeric vector of the same length as x, which determines the thresholds at which the Pickands' estimator will be computed.
type	a character equal to "one-stage" or "two-stage".

Details

The local Pickands' frontier estimator (option type="one-stage"), obtained by applying the well-known approach of Dekkers and de Haan (1989) in conjunction with the transformed sample of z^{xh}_i's described in the function loc_max, is defined as

z^{xh}_{(n-k)} + \left(z^{xh}_{(n-k)}-z^{xh}_{(n-2k)}\right)\{2^{-\log\frac{z^{xh}_{(n-k)}-z^{xh}_{(n-2k)}}{z^{xh}_{(n-2k)}-z^{xh}_{(n-4k)}}/\log 2}-1\}^{-1}.

It is based on three upper order statistics z^{xh}_{(n-k)}, z^{xh}_{(n-2k)}, z^{xh}_{(n-4k)}, and depends on h (see loc_max) as well as an intermediate sequence k=k(x,n)\to\infty with k/n\to 0 as n\to\infty. The two smoothing parameters h and k have to be fixed in the 4th and 5th arguments of the function.

Also, the user can replace each observation y_i in the strip of width 2h around x by the resulting local Pickands', leaving all observations outside the strip unchanged. Then, one may apply the DEA estimator (see the function dea_est) to the obtained transformed data, giving the local DEA estimator (option type="two-stage").

Value

Returns a numeric vector with the same length as x.

Author(s)

Abdelaati Daouia and Thibault Laurent.

References

Dekkers, A.L.M. and L. de Haan (1989). On the estimation of extreme-value index and large quantiles estimation, Annals of Statistics, 17, 1795-1832.

Gijbels, I. and Peng, L. (2000). Estimation of a support curve via order statistics, Extremes, 3, 251-277.

See Also

dea_est

Examples

## Not run: 
data("green")
plot(log(OUTPUT)~log(COST), data=green)
x <- seq(min(log(green$COST)), max(log(green$COST)), length.out=101)
h=0.5
nx<-unlist(lapply(x,function(y) length(which(abs(log(green$COST)-y)<=h))))
k<-trunc(nx^0.1)
lines(x, pick_est(log(green$COST), log(green$OUTPUT), x, h=h, k=k), lty=1, col="red")

## End(Not run)

npbr documentation built on March 31, 2023, 7:45 p.m.