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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
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