Package org.hipparchus.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.

Method Summary

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.

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