org.apache.commons.math.stat.regression

Interface MultipleLinearRegression

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 an n-vector regressand, X is a [n,k] matrix whose k columns are called regressors, b is k-vector of regression parameters and u is an n-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

Version:

$Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $

Method Summary

Modifier and Type	Method and 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.

Method Detail

estimateRegressionParameters

double[] estimateRegressionParameters()

Estimates the regression parameters b.

Returns:

The [k,1] array representing b

estimateRegressionParametersVariance

double[][] estimateRegressionParametersVariance()

Estimates the variance of the regression parameters, ie Var(b).

Returns:

The [k,k] array representing the variance of b

estimateResiduals

double[] estimateResiduals()

Estimates the residuals, ie u = y - X*b.

Returns:

The [n,1] array representing the residuals

estimateRegressandVariance

double estimateRegressandVariance()

Returns the variance of the regressand, ie Var(y).

Returns:

The double representing the variance of y

estimateRegressionParametersStandardErrors

double[] estimateRegressionParametersStandardErrors()

Returns the standard errors of the regression parameters.

Returns:

standard errors of estimated regression parameters