plrm.gcv: Generalized cross-validation bandwidth selection in PLR...

In PLRModels: Statistical Inference in Partial Linear Regression Models

Generalized cross-validation bandwidth selection in PLR models

Description

From a sample {(Y_i, X_{i1}, ..., X_{ip}, t_i): i=1,...,n}, this routine computes an optimal pair of bandwidths for estimating the regression function of the model

Y_i= X_{i1}*\beta_1 +...+ X_{ip}*\beta_p + m(t_i) + \epsilon_i,

where

\beta = (\beta_1,...,\beta_p)

is an unknown vector parameter and

m(.)

is a smooth but unknown function. The optimal pair of bandwidths, (b.opt, h.opt), is selected by means of the generalized cross-validation procedure. The bandwidth b.opt is used in the estimate of \beta, while the pair of bandwidths (b.opt, h.opt) is considered in the estimate of m. Kernel smoothing, combined with ordinary least squares estimation, is used.

Usage

plrm.gcv(data = data, b.equal.h = TRUE, b.seq=NULL, h.seq=NULL, 
num.b = NULL, num.h = NULL, estimator = "NW", kernel = "quadratic")

Arguments

data	data[,1] contains the values of the response variable, Y; data[, 2:(p+1)] contains the values of the "linear" explanatory variables, X_1, ..., X_p; data[, p+2] contains the values of the "nonparametric" explanatory variable, t.
b.equal.h	if TRUE (the default), the same bandwidth is used for estimating both \beta and m.
b.seq	sequence of considered bandwidths, b, in the GCV function for estimating \beta.
h.seq	sequence of considered bandwidths, h, in the pair of bandwidths (b, h) used in the GCV function for estimating m.
num.b	number of values used to build the sequence of considered bandwidths for estimating \beta. If b.seq is not NULL, num.b=length(b.seq). Otherwise, if both num.b and num.h are NULL (the default), num.b=50 is considered; if num.b is NULL (the default) but num.h is not NULL, then num.b=num.h is considered; if b.equal.h=TRUE (the default) and both num.b and num.h are not NULL and different, the maximum value of num.b and num.h is considered for both.
num.h	pairs of bandwidths (b, h) are used for estimating m, num.h being the number of values considered for h. If h.seq is not NULL, num.h=length(h.seq). Otherwise, if both num.b and num.h are NULL (the default), num.h=50 is considered; if num.h is NULL (the default) but num.b is not NULL, num.h=num.b is considered; if b.equal.h=TRUE (the default) and both num.b and num.h are not NULL and different, the maximum value of num.b and num.h is considered for both.
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

The implemented procedure generalizes that one in page 423 in Speckman (1988) by allowing two smoothing parameters instead of only one (see Aneiros-Perez et al., 2004).

Value

bh.opt	selected value for (b,h).
GCV.opt	minimum value of the GCV function.
GCV	matrix containing the values of the GCV function for each pair of bandwidths considered.
b.seq	sequence of considered bandwidths, b, in the GCV function for estimating \beta. If b.seq was not input by the user, it is composed by num.b equidistant values between zero and a quarter of the range of {t_i}.
h.seq	sequence of considered bandwidths, h, in the pair of bandwidths (b, h) used in the GCV function for estimating m. If h.seq was not input by the user, it is composed by num.h equidistant values between zero and a quarter of the range of {t_i}.

Author(s)

German Aneiros Perez [e-mail]

Ana Lopez Cheda [e-mail]

References

Aneiros-Perez, G., Gonzalez-Manteiga, W. and Vieu, P. (2004) Estimation and testing in a partial linear regression under long-memory dependence. Bernoulli 10, 49-78.

Green, P. (1985) Linear models for field trials, smoothing and cross-validation. Biometrika 72, 527-537.

Speckman, P. (1988) Kernel smoothing in partial linear models J. R. Statist. Soc. B 50, 413-436.

See Also

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

Examples

# EXAMPLE 1: REAL DATA

data(barnacles1)
data <- as.matrix(barnacles1)
data <- diff(data, 12)
data <- cbind(data,1:nrow(data))

aux <- plrm.gcv(data)
aux$bh.opt
plot(aux$b.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
beta <- c(0.05, 0.01)
m <- function(t) {0.25*t*(1-t)}
f <- m(t)

x <- matrix(rnorm(200,0,1), nrow=n)
sum <- x%*%beta
epsilon <- rnorm(n, 0, 0.01)
y <-  sum + f + epsilon
data_ind <- matrix(c(y,x,t),nrow=100)

# We obtain the optimal bandwidths
a <-plrm.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.