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

 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")
;
mh = lpderest(x,5)      ; estimate function
;
plot(x,mh)

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

Author: M. Mueller, 20000328
(C) MD*TECH Method and Data Technologies, 05.02.2006