R: Normal optimal choice of smoothing parameter in density...

hnorm {sm} | R Documentation

## 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` | 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.

### Details

See Section 2.4.2 of the reference below.

### Value

the value of the asymptotically optimal smoothing parameter for Normal case.

### Note

As from version 2.1 of the package, a similar effect can be obtained with the new function `h.select`, via `h.select(x, method="normal", weights=weights)` or simply `h.select(x)`. Users are encouraged to adopt this route, since `hnorm` might be not accessible directly in future releases of the package.

### 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.

### See Also

`h.select`, `hcv`, `hsj`

### Examples

    x <- rnorm(50)
    hnorm(x)

[Package sm version 2.2-5.4 Index]