A C E G H I M O S T X Y

A

AbstractInformationCriterionEstimator - Class in org.codehaus.jet.regression.estimators
Abstract base class for implementations of InformationCriterionEstimator
AbstractInformationCriterionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
Creates an AbstractInformationCriterionEstimator with a defaultregression estimator
AbstractInformationCriterionEstimator(MultipleLinearRegressionEstimator) - Constructor for class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
Creates an AbstractInformationCriterionEstimator with a given regression estimator
AbstractMultipleLinearRegressionEstimator - Class in org.codehaus.jet.regression.estimators
Abstract base class for implementations of MultipleLinearRegression
AbstractMultipleLinearRegressionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 
addCovarianceData(double[][]) - Method in class org.codehaus.jet.regression.estimators.GLSMultipleLinearRegressionEstimator
Add the covariance data
addData(double[]) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
 
addData(double[], double[][], double[][]) - Method in class org.codehaus.jet.regression.estimators.GLSMultipleLinearRegressionEstimator
 
addData(double[], double[][], double[][]) - Method in class org.codehaus.jet.regression.estimators.OLSMultipleLinearRegressionEstimator
 
addData(double[]) - Method in interface org.codehaus.jet.regression.InformationCriterionEstimator
Adds sample data
addData(double[], double[][], double[][]) - Method in interface org.codehaus.jet.regression.MultipleLinearRegressionEstimator
Adds sample and covariance data
addXSampleData(double[][]) - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
Adds x sample data
addYSampleData(double[]) - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
Adds y sample data
AkaikeInformationCriterionEstimator - Class in org.codehaus.jet.regression.estimators
Estimator for the Akaike Information Criterion (AIC)
AkaikeInformationCriterionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.AkaikeInformationCriterionEstimator
Creates an AkaikeInformationCriterionEstimator with default regression estimator
AkaikeInformationCriterionEstimator(MultipleLinearRegressionEstimator) - Constructor for class org.codehaus.jet.regression.estimators.AkaikeInformationCriterionEstimator
Creates an AkaikeInformationCriterionEstimator with given regression estimator

C

calculateBeta() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
Calculates the beta of multiple linear regression in matrix notation
calculateBeta() - Method in class org.codehaus.jet.regression.estimators.GLSMultipleLinearRegressionEstimator
Calculates beta by GLS:
calculateBeta() - Method in class org.codehaus.jet.regression.estimators.OLSMultipleLinearRegressionEstimator
Calculates beta by OLS:
calculateBetaVariance() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
Calculates the beta variance of multiple linear regression in matrix notation
calculateBetaVariance() - Method in class org.codehaus.jet.regression.estimators.GLSMultipleLinearRegressionEstimator
Calculates the variance on the beta by GLS:
calculateBetaVariance() - Method in class org.codehaus.jet.regression.estimators.OLSMultipleLinearRegressionEstimator
Calculates the variance on the beta by OLS:
calculateIC(int, int, double) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
 
calculateIC(int, int, double) - Method in class org.codehaus.jet.regression.estimators.AkaikeInformationCriterionEstimator
Calculate AIC
calculateIC(int, int, double) - Method in class org.codehaus.jet.regression.estimators.HannanQuinnInformationCriterionEstimator
Calculate HQIC
calculateIC(int, int, double) - Method in class org.codehaus.jet.regression.estimators.SchwarzInformationCriterionEstimator
Calculate SIC
calculateResiduals() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
Calculates the residuals of multiple linear regression in matrix notation
calculateYVariance(int) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
Calculates the variance on the sample for a given lag order
calculateYVariance() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
Calculates the Y variance of multiple linear regression
calculateYVariance() - Method in class org.codehaus.jet.regression.estimators.GLSMultipleLinearRegressionEstimator
Calculates the variance on the y by GLS:
calculateYVariance() - Method in class org.codehaus.jet.regression.estimators.OLSMultipleLinearRegressionEstimator
Calculates the variance on the Y by OLS:
createDefaultRegressionEstimator() - Static method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
 

E

estimateIC(int) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
 
estimateIC(int) - Method in interface org.codehaus.jet.regression.InformationCriterionEstimator
Estimates the IC value for a given lag order
estimateRegressandVariance() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 
estimateRegressandVariance() - Method in interface org.codehaus.jet.regression.MultipleLinearRegressionEstimator
Returns the variance of the regressand, ie Var(y)
estimateRegressionParameters() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 
estimateRegressionParameters() - Method in interface org.codehaus.jet.regression.MultipleLinearRegressionEstimator
Estimates the regression parameters b
estimateRegressionParametersVariance() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 
estimateRegressionParametersVariance() - Method in interface org.codehaus.jet.regression.MultipleLinearRegressionEstimator
Estimates the variance of the regression parameters, ie Var(b)
estimateResiduals() - Method in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 
estimateResiduals() - Method in interface org.codehaus.jet.regression.MultipleLinearRegressionEstimator
Estimates the residuals, ie u = y - X*b

G

getSampleSize() - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
 
GLSMultipleLinearRegressionEstimator - Class in org.codehaus.jet.regression.estimators
The GLS implementation of the multiple linear regression GLS assumes a general covariance matrix Omega of the error
GLSMultipleLinearRegressionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.GLSMultipleLinearRegressionEstimator
 

H

HannanQuinnInformationCriterionEstimator - Class in org.codehaus.jet.regression.estimators
Estimator for the Hannan-Quinn Information Criterion (HQIC)
HannanQuinnInformationCriterionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.HannanQuinnInformationCriterionEstimator
Creates an HannanQuinnInformationCriterionEstimator with default regression estimator
HannanQuinnInformationCriterionEstimator(MultipleLinearRegressionEstimator) - Constructor for class org.codehaus.jet.regression.estimators.HannanQuinnInformationCriterionEstimator
Creates an HannanQuinnInformationCriterionEstimator with given regression estimator

I

InformationCriterionEstimator - Interface in org.codehaus.jet.regression
An autoregressive (AR) process can be represented as

M

minimiseIC(int, int) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
 
minimiseIC(int, int) - Method in interface org.codehaus.jet.regression.InformationCriterionEstimator
Minimise the IC value for a given lag order interval
MultipleLinearRegressionEstimator - Interface in org.codehaus.jet.regression
The multiple linear regression can be represented in matrix-notation as

O

OLSMultipleLinearRegressionEstimator - Class in org.codehaus.jet.regression.estimators
The OLS implementation of the multiple linear regression OLS assumes the covariance matrix of the error to be diagonal and with equal variance.
OLSMultipleLinearRegressionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.OLSMultipleLinearRegressionEstimator
 
org.codehaus.jet.regression - package org.codehaus.jet.regression
 
org.codehaus.jet.regression.estimators - package org.codehaus.jet.regression.estimators
 

S

SchwarzInformationCriterionEstimator - Class in org.codehaus.jet.regression.estimators
Estimator for the Schwarz Information Criterion (SIC)
SchwarzInformationCriterionEstimator() - Constructor for class org.codehaus.jet.regression.estimators.SchwarzInformationCriterionEstimator
Creates an SchwarzInformationCriterionEstimator with default regression estimator
SchwarzInformationCriterionEstimator(MultipleLinearRegressionEstimator) - Constructor for class org.codehaus.jet.regression.estimators.SchwarzInformationCriterionEstimator
Creates an SchwarzInformationCriterionEstimator with given regression estimator

T

toRegressands(double[], int) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
Converts sample to regression regressand
toRegressors(double[], int) - Method in class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
Converts sample to regression regressors

X

X - Variable in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 

Y

Y - Variable in class org.codehaus.jet.regression.estimators.AbstractMultipleLinearRegressionEstimator
 

A C E G H I M O S T X Y

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