XploRe Help : lpderest

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:	lpregest lpderrot

Quantlet:		lpderest
Description:		estimates the q-th derivative of a regression function using local polynomial kernel regression. The computation uses WARPing.

Reference(s):

Fan and Gijbels (1995): Local Polynomial Fitting Fan and Marron (1994): Binning for local polynomials Haerdle (1991): Smoothing Techniques

Usage:	`mh = lpderest (x, h {,q {,p {,K} {,d}}})`
Input:
	`x`	n x 2, the data. In the first column the independent, in the second column the dependent variable.
	`h`	scalar, bandwidth. If not given, the rule of thumb bandwidth computed by lpderrot is used.
	`q`	integer <=4, order of derivative. If not given, q=1 (first derivative) is used.
	`p`	integer, order of polynomial. If not given, p=q+1 is used.
	`K`	string, kernel function on [-1,1] or Gaussian kernel "gau". If not given, the Quartic kernel "qua" is used.
	`d`	scalar, discretization binwidth. d must be smaller than h. If not given, the minimum of h/3 and (max(x[,1])-min(x[,1]))/100 is used.
Output:
	`mh`	m x 2 matrix, the first column is a grid and the second column contains the derivative estimate on that grid.

Example:

library("smoother") library("plot") ; x = read("motcyc.dat") mh = lpderest(x,5) ; estimate function ; mh = setmask(mh, "line","blue") plot(x,mh)

Result:

The derivative regession estimate (blue) using
Quartic kernel and bandwidth h=5 is pictured.

Author: M. Mueller, 20000328