select.k.func: Selection of the Tuning Parameter k

In EXRQ: Extreme Regression of Quantiles

Description

This function selects the tuning parameter k, the number of upper order statistics involved in Hill estimator of EVI among a grid of points following the method described in Section 3.3 of Wang and Li (2013). The method selects k as the value that minimizes the discrepancy between the estimated x-dependent EVI on the transformed scale and lam times the estimated x-dependent EVI on the original scale

Usage

select.k.func(y, x, Lam.y, lam, a, max.tau, grid.k, n)

Arguments

y	a vector of n untransformed responses
x	a n x p matrix of n observations and p predictors
Lam.y	a vector of n power-transformed responses
lam	the power-transformation parameter
a	location shift parameter in the power transformation (introduced to avoid negative y values)
max.tau	the upper bound of the intermediate quantile levels
grid.k	the grid for the number of upper order statistics involved in Hill estimator
n	the number of observations

Value

the selected k is returned

References

Wang, H. and Li, D. (2013). Estimation of conditional high quantiles through power transformation. Journal of the American Statistical Association, 108, 1062-1074.

EXRQ documentation built on May 1, 2019, 7:26 p.m.