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

Constructor Summary

Constructors

Constructor and Description
GLSMultipleLinearRegression()

Method Summary

Modifier and Type	Method and Description
void	newSampleData(double[] y, double[][] x, double[][] covariance) Replace sample data, overriding any previous sample.

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

estimateErrorVariance, estimateRegressandVariance, estimateRegressionParameters, estimateRegressionParametersStandardErrors, estimateRegressionParametersVariance, estimateRegressionStandardError, estimateResiduals, isNoIntercept, newSampleData, setNoIntercept

Methods inherited from class java.lang.Object

equals, 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)

Replace sample data, overriding any previous sample.

Parameters:

y - y values of the sample

x - x values of the sample

covariance - array representing the covariance matrix