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

java.lang.Object
  extended by org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression
      extended by 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
GLSMultipleLinearRegression()
           
 
Method Summary
 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


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