confidence_intervals_LP: Asymptotic confidence intervals for the local polynomial...

In svjaco/lpreba: Local Polynomial Regression with Option for Explicit Boundary Adjustment

Description

Asymptotic confidence intervals for the local polynomial estimator

Usage

confidence_intervals_LP(
  x,
  X,
  Y,
  kernel = epanechnikov,
  bw,
  degree = 1L,
  alpha = 0.05
)

Arguments

x	Evaluation points (vector).
X	Data for the regressor (vector).
Y	Data for the regressand (vector).
kernel	Kernel (function). Default is epanechnikov.
bw	Bandwidth (scalar).
degree	Degree of the locally fitted polynomial (integer). Default is 1L.
alpha	Significance level (scalar). Default is 0.05.

Value

List containing:

confidence_intervals_lower	Lower points for the (1 - alpha) asymptotic confidence intervals (vector).
confidence_intervals_upper	Upper points for the (1 - alpha) asymptotic confidence intervals (vector).

Examples

m_fun <- function(x) {sin(2*pi*x)} # True regression function
n <- 100 # Sample size
X <- seq(0, 1, length.out = n) # Data for the regressor
m_X <- m_fun(X) # True values of regression function
epsilon <- rnorm(n, sd = 0.25) # Error term
Y <- m_X + epsilon # Data for the regressand
bw <- 0.2 # Bandwidth
x <- seq(0, 1, length.out = n/2) # Evaluation points

output_confidence_intervals_LP <- confidence_intervals_LP(x = x, X = X, Y = Y, bw = bw,
                                                          alpha = 0.05)
confidence_intervals_lower <- output_confidence_intervals_LP$confidence_intervals_lower
confidence_intervals_upper <- output_confidence_intervals_LP$confidence_intervals_upper

svjaco/lpreba documentation built on March 4, 2022, 12:42 a.m.