Library: | smoother |
See also: | denbwsel denrot denest |
Quantlet: | denbwcrit | |
Description: | determines the optimal from a range of bandwidths by one of the following bandwidth selection criteria: Least Squares Cross Validation (lscv), Biased Cross Validation (bcv), Smoothed Cross Validation (scv), Jones, Marron and Park Cross Validation (jmp), Park and Marron Plug-in (pm), Sheather and Jones Plug-in (sj), and Silverman's rule of thumb. |
Haerdle (1991): Smoothing Techniques
Haerdle, Mueller, Sperlich, Werwatz (1999): Non- and Semiparametric Modelling
Usage: | {hopt, ch} = denbwcrit(crit, x {,h {,K} {,d} }) | |
Input: | ||
crit | string, criterion for bandwidth selection: "lscv", "bcv", "scv", "jmp", "pm", "sj". | |
x | n x 1 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, "gau" 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))/200 is used. | |
Output: | ||
hopt | scalar, optimal bandwidth. (If negative in case of "pm" or "sj", denbwcrit needs to be run again with different h range.) | |
ch | m x 2 vector, the criterion function for h values. |
library("smoother") x=normal(500) h=grid(0.05,0.1,10) {hopt,ch}=denbwcrit("lscv",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: | denbwsel denrot denest |