XploRe Help : denbwsel

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library:	smoother
See also:	denest denci dencb denbwcrit denrot canker

Quantlet:		denbwsel
Description:		interactive tool for bandwidth selection in univariate kernel density estimation.

Reference(s):

Park, B. V. and Turlach, B. A. (1992). Practical Performance of Several Data Driven Bandwidth Selectors. Computational Statistics, 7: pp. 251-270. Haerdle, W. (1991). Smoothing Techniques. With Implementations in S. Springer, New York, p. 220. Haerdle, Mueller, Sperlich, Werwatz (1999): Non- and Semiparametric Modelling. XploRe e-book.

Usage:	`{hcrit,crit}= denbwsel(x {,h {,K} {,d} })`
Input:
	`x`	n x 1 vector, the data.
	`h`	opional m x 1 vector, vector of bandwidths.
	`K`	optional string, kernel function on [-1,1], default = "gau"
	`d`	optional 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:
	`hcrit`	p x 1 vector, selected bandwidths by the different criteria.
	`crit`	p x 1 string vector, criteria considered for bandwidth selection.

Note:

Internally, the Gaussian kernel is used for all computations. If a different kernel function K is given as input, the asymptotical equivalent bandwidth for K is calculated via the canonical kernel approach.

Example:

library("smoother") x=normal(500) tmp=denbwsel(x)

Result:

You may interactively choose the bandwidth
selector. The parameters (range of bandwidths,
kernel K, binwidth d) can be changed as well.

Author: M. Bianchi, S. Klinke, L. Yang, M. Mueller, 20020915 license MD*Tech