This functions evaluates the smoothing parameter which is asymptotically optimal for estimating a density function when the underlying distribution is Normal. Data in one, two or three dimensions can be handled.
hnorm(x, weights)
the value of the asymptotically optimal smoothing parameter for Normal case.
a vector, or matrix with two or three columns, containing the data.
an optional vector of integer values which allows the kernel functions over the observations to take different weights when they are averaged to produce a density estimate. This is useful, in particular, for censored data and to construct an estimate from binned data.
See Section 2.4.2 of the reference below.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
h.select
, hcv
, hsj