h_thumbBw {locpolExpectile}

Quantile-based bandwidth selectors based on the Rule-of-Thumb selector for mean regression

Description

Quantile-based bandwidth selector for univariate expectile regression based on the Rule-of-Thumb for mean regression proposed by Fan and Gijbels (1996).

Usage

h_thumbBw(X, Y, p = 1, kernel = gaussK, omega)

Arguments

X

The covariate data values.

Y

The response data values.

p

The order of the local polynomial estimator. In default setting, p=1.

kernel

The kernel used to perform the estimation. In default setting, kernel=gaussK. See details in Kernels.

omega

Numeric vector of level between 0 and 1 where 0.5 corresponds to the mean.

Value

h_thumbBw provides the quantile-based bandwidth based on the Rule-of-Thumb for mean regression proposed by Fan and Gijbels (1996) as discussed in Adam and Gijbels (2021a).

References

Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and Its Applications. Number 66 in Monographs on statistics and applied probability series. Chapman and Hall, London.

Adam, C. and Gijbels, I. (2021a). Local polynomial expectile regression. Annals of the Institute of Statistical Mathematics doi:10.1007/s10463-021-00799-y.

Examples

library(locpol)
data(mcycle)
y=mcycle$accel
x=mcycle$times

h=h_thumbBw(X=x,Y=y,p=1,kernel=gaussK,omega=0.1)
#h=1.824103