lm.fit.recursive

This function fits a linear model by recursive least squares. It is
a utility routine for the <code>KhmaladzeTest</code> function of the quantile regression
package.

methods

Estimation and inference methods for models for conditional quantile functions:
Linear and nonlinear parametric and non-parametric (total variation penalized) models
for conditional quantiles of a univariate response and several methods for handling
censored survival data. Portfolio selection methods based on expected shortfall
risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press,
<doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression,
CRC Press, <doi:10.1201/9781315120256>.

Roger Koenker

quantreg

Quantile Regression

Stephen Portnoy

Blaise Melly

Achim Zeileis

Philip Grosjean

Cleve Moler

Yousef Saad

Victor Chernozhukov

Ivan Fernandez-Val

Martin Maechler

lm.fit.recursive function

<dl> <dt>X</dt>
<dd>Design Matrix</dd> <dt>y</dt>
<dd>Response Variable</dd> <dt>int</dt>
<dd>if TRUE then append intercept to X</dd></dl>

Arguments

Author

Recursive Least Squares — lm.fit.recursive

<dl>

 <dt>X</dt>
<dd>Design Matrix</dd>

 <dt>y</dt>
<dd>Response Variable</dd>

 <dt>int</dt>
<dd>if TRUE then append intercept to X</dd>

</dl>

Recursive Least Squares

lm.fit.recursive: Recursive Least Squares

Description

Usage

Value

Arguments

Author

References