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

Description Usage Arguments Details Value References See Also Examples

Description

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

Usage

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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

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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")

Example output

Package 'sm', version 2.2-5.4: type help(sm) for summary information

sm documentation built on May 29, 2017, 10:38 a.m.

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