np.gcv: Generalized cross-validation bandwidth selection in...

In PLRModels: Statistical Inference in Partial Linear Regression Models

Generalized cross-validation bandwidth selection in nonparametric regression models

Description

From a sample {(Y_i, t_i): i=1,...,n}, this routine computes an optimal bandwidth for estimating m in the regression model

Y_i= m(t_i) + \epsilon_i.

The regression function, m, is a smooth but unknown function. The optimal bandwidth is selected by means of the generalized cross-validation procedure. Kernel smoothing is used.

Usage

np.gcv(data = data, h.seq=NULL, num.h = 50, estimator = "NW", 
kernel = "quadratic")

Arguments

data	data[, 1] contains the values of the response variable, Y; data[, 2] contains the values of the explanatory variable, t.
h.seq	sequence of considered bandwidths in the GCV function. If NULL (the default), num.h equidistant values between zero and a quarter of the range of t_i are considered.
num.h	number of values used to build the sequence of considered bandwidths. If h.seq is not NULL, num.h=length(h.seq). Otherwise, the default is 50.
estimator	allows us the choice between “NW” (Nadaraya-Watson) or “LLP” (Local Linear Polynomial). The default is “NW”.
kernel	allows us the choice between “gaussian”, “quadratic” (Epanechnikov kernel), “triweight” or “uniform” kernel. The default is “quadratic”.

Details

See Craven and Wahba (1979) and Rice (1984).

Value

h.opt	selected value for the bandwidth.
GCV.opt	minimum value of the GCV function.
GCV	vector containing the values of the GCV function for each considered bandwidth.
h.seq	sequence of considered bandwidths in the GCV function.

Author(s)

German Aneiros Perez [e-mail]

Ana Lopez Cheda [e-mail]

References

Craven, P. and Wahba, G. (1979) Smoothing noisy data with spline functions. Numer. Math. 31, 377-403.

Rice, J. (1984) Bandwidth choice for nonparametric regression. Ann. Statist. 12, 1215-1230.

See Also

Other related functions are: np.est, np.cv, plrm.est, plrm.gcv and plrm.cv.

Examples

# EXAMPLE 1: REAL DATA
data <- matrix(10,120,2)
data(barnacles1)
barnacles1 <- as.matrix(barnacles1)
data[,1] <- barnacles1[,1]
data <- diff(data, 12)
data[,2] <- 1:nrow(data)

aux <- np.gcv(data)
aux$h.opt
plot(aux$h.seq, aux$GCV, xlab="h", ylab="GCV", type="l")



# EXAMPLE 2: SIMULATED DATA
## Example 2a: independent data

set.seed(1234)
# We generate the data
n <- 100
t <- ((1:n)-0.5)/n
m <- function(t) {0.25*t*(1-t)}
f <- m(t)

epsilon <- rnorm(n, 0, 0.01)
y <-  f + epsilon
data_ind <- matrix(c(y,t),nrow=100)

# We apply the function
a <-np.gcv(data_ind)
a$GCV.opt

GCV <- a$GCV
h <- a$h.seq
plot(h, GCV, type="l")

PLRModels documentation built on Aug. 19, 2023, 5:10 p.m.