public class GLSMultipleLinearRegression extends AbstractMultipleLinearRegression
u ~ N(0, Omega)Estimated by GLS,
b=(X' Omega^-1 X)^-1X'Omega^-1 ywhose variance is
Var(b)=(X' Omega^-1 X)^-1
| Constructor and Description |
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GLSMultipleLinearRegression() |
| Modifier and Type | Method and Description |
|---|---|
protected RealVector |
calculateBeta()
Calculates beta by GLS.
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protected RealMatrix |
calculateBetaVariance()
Calculates the variance on the beta.
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protected double |
calculateErrorVariance()
Calculates the estimated variance of the error term using the formula
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protected RealMatrix |
getOmegaInverse()
Get the inverse of the covariance.
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protected void |
newCovarianceData(double[][] omega)
Add the covariance data.
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void |
newSampleData(double[] y,
double[][] x,
double[][] covariance)
Replace sample data, overriding any previous sample.
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calculateResiduals, calculateYVariance, estimateErrorVariance, estimateRegressandVariance, estimateRegressionParameters, estimateRegressionParametersStandardErrors, estimateRegressionParametersVariance, estimateRegressionStandardError, estimateResiduals, getX, getY, isNoIntercept, newSampleData, newXSampleData, newYSampleData, setNoIntercept, validateCovarianceData, validateSampleDatapublic void newSampleData(double[] y,
double[][] x,
double[][] covariance)
y - y values of the samplex - x values of the samplecovariance - array representing the covariance matrixprotected void newCovarianceData(double[][] omega)
omega - the [n,n] array representing the covarianceprotected RealMatrix getOmegaInverse()
The inverse of the covariance matrix is lazily evaluated and cached.
protected RealVector calculateBeta()
b=(X' Omega^-1 X)^-1X'Omega^-1 y
calculateBeta in class AbstractMultipleLinearRegressionprotected RealMatrix calculateBetaVariance()
Var(b)=(X' Omega^-1 X)^-1
calculateBetaVariance in class AbstractMultipleLinearRegressionprotected double calculateErrorVariance()
Var(u) = Tr(u' Omega^-1 u)/(n-k)where n and k are the row and column dimensions of the design matrix X.
calculateErrorVariance in class AbstractMultipleLinearRegressionCopyright © 2003–2016 The Apache Software Foundation. All rights reserved.