hnorm: Normal optimal choice of smoothing parameter in density estimation
Description
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.
Usage
hnorm(x, weights)
Arguments
x
a vector, or matrix with two or three columns, containing the data.
weights
a vector 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.
Value
the value of the Normal optimal smoothing parameter.
Details
See Section 2.4.2 of the reference below.
References
Bowman, A.W. and Azzalini, A. (1997).
Applied Smoothing Techniques for Data Analysis:the Kernel Approach with S-Plus Illustrations.
Oxford University Press, Oxford.