XploRe Help : lpregest

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:	lpderest lpregrot

Quantlet:		lpregest
Description:		estimates 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 = lpregest (x, h {,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 lpregrot is used.
	`p`	integer, order of polynomial. If not given, p=1 (local linear) is used. p=0 yields the Nadaraya-Watson estimator.
	`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 regression estimate on that grid.

Example:

library("smoother") library("plot") ; x = 4.*pi.*(uniform(200)-0.5) ; independent variable m = cos(x) ; true function e = uniform(200)-0.5 ; error term x = x~(m+e) ; mh = lpregest(x,1) ; estimate function ; mh = setmask(mh, "line","blue") m = setmask(sort(x[,1]~m) , "line","black","thin") plot(x,mh,m)

Result:

The Nadaraya-Watson regession estimate (blue) using
Quartic kernel and bandwidth h=1 and the true
regression function (thin black) are pictured.

Author: M. Mueller, 20000328