atRisk (version 0.2.0)

f_nadaraya_watson_quantile: Estimation of quantiles using the Nadaraya-Watson estimator with a product kernel

Description

This function performs quantile regression using the Nadaraya-Watson estimator with a product kernel. It computes the weights using a Gaussian kernel for each dimension of the explanatory variables and then estimates the quantile using a weighted average of the observed responses.

Usage

f_nadaraya_watson_quantile(v_dep, v_expl, qt_trgt, bandwidth)

Value

Numeric matrix with all the predicted values based on each quantile regression, where each column corresponds to a quantile target.

Arguments

v_dep

Numeric vector of the dependent variable

v_expl

Numeric vector or matrix of the (k) explanatory covariate(s)

qt_trgt

Numeric vector, dim k, of k quantiles for different qt-estimations

bandwidth

Numeric value specifying the bandwidth for the Gaussian kernel

Examples

# Data process
set.seed(123)
Y <- as.vector(rnorm(100))
X <- matrix(rnorm(200), ncol = 2)
quantile_target <- c(0.1, 0.5, 0.9)
bandwidth_value <- 0.5

results_qt <- f_nadaraya_watson_quantile(v_dep=Y, 
v_expl=X, 
qt_trgt=quantile_target, 
bandwidth=bandwidth_value)