org.apache.commons.math.stat.regression
Class GLSMultipleLinearRegression

java.lang.Object
  org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression
      org.apache.commons.math.stat.regression.GLSMultipleLinearRegression

All Implemented Interfaces:

MultipleLinearRegression

public class GLSMultipleLinearRegression
extends AbstractMultipleLinearRegression

The GLS implementation of the multiple linear regression. GLS assumes a general covariance matrix Omega of the error

 u ~ N(0, Omega)
 

Estimated by GLS,

 b=(X' Omega^-1 X)^-1X'Omega^-1 y
 

whose variance is

 Var(b)=(X' Omega^-1 X)^-1
 

Since:

2.0

Version:

$Revision: 676241 $ $Date: 2008-07-12 23:41:17 +0200 (sam., 12 juil. 2008) $

Field Summary

Fields inherited from class org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression

X, Y

Constructor Summary

GLSMultipleLinearRegression()

Method Summary

protected RealMatrix	calculateBeta() Calculates beta by GLS.
protected RealMatrix	calculateBetaVariance() Calculates the variance on the beta by GLS.
protected double	calculateYVariance() Calculates the variance on the y by GLS.
protected void	newCovarianceData(double[][] omega) Add the covariance data.
void	newSampleData(double[] y, double[][] x, double[][] covariance)

Methods inherited from class org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression

calculateResiduals, estimateRegressandVariance, estimateRegressionParameters, estimateRegressionParametersVariance, estimateResiduals, newSampleData, newXSampleData, newYSampleData, validateCovarianceData, validateSampleData

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

GLSMultipleLinearRegression

public GLSMultipleLinearRegression()

Method Detail

newSampleData

public void newSampleData(double[] y,
                          double[][] x,
                          double[][] covariance)

newCovarianceData

protected void newCovarianceData(double[][] omega)

Add the covariance data.

Parameters:

omega - the [n,n] array representing the covariance

calculateBeta

protected RealMatrix calculateBeta()

Calculates beta by GLS.

  b=(X' Omega^-1 X)^-1X'Omega^-1 y
 

Specified by:

calculateBeta in class AbstractMultipleLinearRegression

Returns:

beta

calculateBetaVariance

protected RealMatrix calculateBetaVariance()

Calculates the variance on the beta by GLS.

  Var(b)=(X' Omega^-1 X)^-1
 

Specified by:

calculateBetaVariance in class AbstractMultipleLinearRegression

Returns:

The beta variance matrix

calculateYVariance

protected double calculateYVariance()

Calculates the variance on the y by GLS.

  Var(y)=Tr(u' Omega^-1 u)/(n-k)
 

Specified by:

calculateYVariance in class AbstractMultipleLinearRegression

Returns:

The Y variance