Interface MultipleLinearRegression
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- All Known Implementing Classes:
AbstractMultipleLinearRegression
,GLSMultipleLinearRegression
,OLSMultipleLinearRegression
public interface MultipleLinearRegression
The multiple linear regression can be represented in matrix-notation.y=X*b+u
where y is ann-vector
regressand, X is a[n,k]
matrix whosek
columns are called regressors, b isk-vector
of regression parameters andu
is ann-vector
of error terms or residuals. The notation is quite standard in literature, cf eg Davidson and MacKinnon, Econometrics Theory and Methods, 2004.- Since:
- 2.0
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description double
estimateRegressandVariance()
Returns the variance of the regressand, ie Var(y).double[]
estimateRegressionParameters()
Estimates the regression parameters b.double[]
estimateRegressionParametersStandardErrors()
Returns the standard errors of the regression parameters.double[][]
estimateRegressionParametersVariance()
Estimates the variance of the regression parameters, ie Var(b).double[]
estimateResiduals()
Estimates the residuals, ie u = y - X*b.
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Method Detail
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estimateRegressionParameters
double[] estimateRegressionParameters()
Estimates the regression parameters b.- Returns:
- The [k,1] array representing b
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estimateRegressionParametersVariance
double[][] estimateRegressionParametersVariance()
Estimates the variance of the regression parameters, ie Var(b).- Returns:
- The [k,k] array representing the variance of b
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estimateResiduals
double[] estimateResiduals()
Estimates the residuals, ie u = y - X*b.- Returns:
- The [n,1] array representing the residuals
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estimateRegressandVariance
double estimateRegressandVariance()
Returns the variance of the regressand, ie Var(y).- Returns:
- The double representing the variance of y
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estimateRegressionParametersStandardErrors
double[] estimateRegressionParametersStandardErrors()
Returns the standard errors of the regression parameters.- Returns:
- standard errors of estimated regression parameters
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