MultipleLinearRegression
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 | Description |
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GLSMultipleLinearRegression() |
Modifier and Type | Method | Description |
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protected RealVector |
calculateBeta() |
Calculates beta by GLS.
|
protected RealMatrix |
calculateBetaVariance() |
Calculates the variance on the beta.
|
protected double |
calculateErrorVariance() |
Calculates the estimated variance of the error term using the formula
|
protected RealMatrix |
getOmegaInverse() |
Get the inverse of the covariance.
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protected void |
newCovarianceData(double[][] omega) |
Add the covariance data.
|
void |
newSampleData(double[] y,
double[][] x,
double[][] covariance) |
Replace sample data, overriding any previous sample.
|
calculateResiduals, calculateYVariance, estimateErrorVariance, estimateRegressandVariance, estimateRegressionParameters, estimateRegressionParametersStandardErrors, estimateRegressionParametersVariance, estimateRegressionStandardError, estimateResiduals, getX, getY, isNoIntercept, newSampleData, newXSampleData, newYSampleData, setNoIntercept, validateCovarianceData, validateSampleData
public 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 AbstractMultipleLinearRegression
protected RealMatrix calculateBetaVariance()
Var(b)=(X' Omega^-1 X)^-1
calculateBetaVariance
in class AbstractMultipleLinearRegression
protected 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 AbstractMultipleLinearRegression
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