lprOperator

Construct a matrix based on the local polynomial estimation at 
 a corresponding sequence of x data and sequence of gridpoint z.

models

Functions and data sets for data sharpening.
Nonparametric regressions are computed subject to smoothness
and other kinds of penalties.

D. Wang

sharpPen

Penalized Data Sharpening for Local Polynomial Regression

W.J. Braun

X.J. Hu

lprOperator function

<dl> <dt>h</dt>
<dd>the kernel bandwidth smoothing parameter.</dd> <dt>xx</dt>
<dd>numeric vector of x data. Missing values are not accepted.</dd> <dt>zz</dt>
<dd>numeric vector of gridpoint z data. Missing values are not accepted.</dd> <dt>p</dt>
<dd>degree of local polynomial used.</dd></dl>

Arguments

Author

Local Polynomial Estimator Matrix Construction — lprOperator

<dl>

 <dt>h</dt>
<dd>the kernel bandwidth smoothing parameter.</dd>

 <dt>xx</dt>
<dd>numeric vector of x data. Missing values are not accepted.</dd>

 <dt>zz</dt>
<dd>numeric vector of gridpoint z data. Missing values are not accepted.</dd>

 <dt>p</dt>
<dd>degree of local polynomial used.</dd>

</dl>

lprOperator: Local Polynomial Estimator Matrix Construction

Description

Usage

Value

Arguments

Author