sm (version 2.0-2)

nmise: 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.

Value

• the mean integrated squared error of the density estimate.

Details

see Section 2.4 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.

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")