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: regxci regxcb regest regxbwsel lpregxest regxestp

Quantlet: regxest
Description: computes the Nadaraya-Watson estimator for univariate regression.


Usage: mh = regxest(x {,h {,K} {,v} })
x n x 2 matrix, the data. The first column contains the independent and the second column the dependent variable.
h optional scalar, bandwidth. If not given, 20% of the range of x[,1] is used as default.
K optional string, kernel function on [-1,1] or Gaussian kernel "gau". If not given, the Quartic kernel "qua" is used as default.
v optional m x 1 vector, values of the independent variable on which to compute the regression. If not given, the (sorted) x is used as default.
mh n x 2 or m x 2 matrix, the first column represents the sorted first column of x or the sorted v and the second column contains the regression estimate on the values of the first column.


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 = regxest(x,1)                ; estimate function
mh = setmask(mh, "line","blue")
m  = setmask(sort(x[,1]~m) , "line","black","thin")

The Nadaraya-Watson regession estimate (blue line) using
Quartic kernel and bandwidth h = 1 and the true
regression function (thin black line) are pictured.
x = read("motcyc")            ; read motorcycle data
mhe = regxest(x,3,"epa")      ; estimate function
mhu = regxest(x,2,"uni")      ; estimate function
mhe= setmask(mhe,"line","green")
mhu= setmask(mhu,"line","red")
plot(x,mhe,mhu)               ; graph functions

The Nadaraya-Watson regession estimates using
Epanechnikov kernel (green line) and Uniform kernel
(red line) are pictured.

Author: M. Mueller, 20020915
(C) MD*TECH Method and Data Technologies, 05.02.2006