Cross-validation criterion for nonparametric density estimation
Usage
cv(x, h, h.weights=NA)
Arguments
x
|
vector, or two-column matrix, of data.
|
h
|
a smoothing parameter. In the two-dimensional case this is multiplied
by the standard deviation of each component to produce two smoothing
parameters
|
h.weights
|
a vector of weights which multiply the smoothing parameter(s) used in the
kernel function at each observation.
|
Description
This function computes a cross-validatory criterion, based on integrated
squared error, for use in selecting a smoothing parameter in nonparametric
density estimation.Details
see Section 2.4.3 of the reference below. The function is called
automatically by hcv
and does not usually need to be called
independently.Value
The value of the cross-validatory criterion.Side Effects
NoneReferences
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for
Data Analysis: the Kernel Approach with S-Plus Illustrations.
Oxford University Press, Oxford.See Also
hcv
, hsj
, hnorm
, sj
Examples
x <- rnorm(50)
hgrid <- seq(0.1, 1, length = 10)
cvgrid <- vector("numeric", length = length(hgrid))
for (i in 1:10) cvgrid[i] <- cv(x, hgrid[i])
plot(hgrid, cvgrid, type="l")