Local Polynomial Regression Smoother Matrix

Compute the Local Polynomial Regression smoother matrix of degree p. Special cases: p=0 is Nadaraya-Watson, p=1 is Local Linear Regression.

Usage

S.LPR(tt, h, p = 1, Ker = "norm", w = NULL, cv = FALSE)

Arguments

tt

Evaluation points (numeric vector).

h

Bandwidth parameter.

p

Polynomial degree (default 1 for local linear).

Ker

Kernel function or name.

w

Optional weights vector.

cv

Logical. If TRUE, compute leave-one-out cross-validation matrix.

Value

An n x n smoother matrix S.

Examples

tt <- seq(0, 1, length.out = 50)
S <- S.LPR(tt, h = 0.1, p = 2)  # Local quadratic regression