

   mmeeaann iinntteeggrraatteedd ssqquuaarreedd eerrrroorr ffoorr ddeennssiittyy eessttiimmaattiioonn wwiitthh
   nnoorrmmaall ddaattaa

        nmise(sd, n, h)

   AArrgguummeennttss::

         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 den-
             sity estimate.

   DDeessccrriippttiioonn::

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

   DDeettaaiillss::

        see Section 2.4 of the reference below.

   VVaalluuee::

        the mean integrated squared error of the density esti-
        mate.

   SSiiddee EEffffeeccttss::

        none.

   RReeffeerreenncceess::

        Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing
        Techniques for Data Analysis: the Kernel Approach with
        S-Plus Illustrations.  Oxford University Press, Oxford.

   SSeeee AAllssoo::

        `nise'

   EExxaammpplleess::

        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")

