| Library: | smoother |
| See also: | regbwsel regxbwsel regest |
| Macro: | regbwcrit | |
| Description: | determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice). The computation uses WARPing. |
Haerdle, Mueller, Sperlich, Werwatz (1999): Non- and Semiparametric Modelling
| Usage: | {hopt, ch} = regbwcrit(crit, x {,h {,K} {,d} }) | |
| Input: | ||
| crit | string, criterion for bandwidth selection: "shi", "gcv", "aic", "fpe", "rice". | |
| x | n x 2 vector, the data. | |
| h | m x 1 vector, vector of bandwidths. | |
| K | string, kernel function on [-1,1] or Gaussian kernel "gau". If not given, "qua" is used. | |
| d | scalar, discretization binwidth. d must be smaller than h. If not given, the minimum of min(h)/3 and (max(x)-min(x))/500 is used. | |
| Output: | ||
| hopt | scalar, optimal bandwidth. | |
| ch | m x 2 vector, the criterion function for h values. | |
library("smoother")
x=read("nicfoo")
h=grid(0.05,0.1,10)
{hopt,ch}=regbwcrit("gcv",x,h)
hopt
library("plot")
ch=setmask(ch,"line","blue")
plot(ch)
setgopt(plotdisplay,1,1,"title",string("hopt=%1.6g",hopt))
hopt is the LSCV optimal bandwidth for these data. The resulting curve for the LSCV criterion is plotted.
| Library: | smoother |
| See also: | regbwsel regxbwsel regest |