R: mean integrated squared error for density estimation with...

nmise {sm} | R Documentation

## mean integrated squared error for density estimation with normal data

### Description

This function evaluates the mean integrated squared error of a density estimate which is constructed from data which follow a normal distribution.

### Usage

    nmise(sd, n, h)

### Arguments

`sd` | the standard deviation of the normal distribution from which the data arise.
`n` | the sample size of the data.
`h` | the smoothing parameter used to construct the density estimate.

### Details

see Section 2.4 of the reference below.

### Value

the mean integrated squared error of the density estimate.

### 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

`nise`

### Examples

    x  <- rnorm(50)
    sd <- sqrt(var(x))
    n  <- length(x)
    h  <- seq(0.1, 2, length=32)
    plot(h, nmise(sd, n, h), type = "l")

[Package sm version 2.2-5.4 Index]