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java.lang.Objectorg.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
public abstract class AbstractInformationCriterionEstimator
Abstract base class for implementations of InformationCriterionEstimator
Constructor Summary | |
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protected |
AbstractInformationCriterionEstimator()
Creates an AbstractInformationCriterionEstimator with a defaultregression estimator |
protected |
AbstractInformationCriterionEstimator(MultipleLinearRegressionEstimator regression)
Creates an AbstractInformationCriterionEstimator with a given regression estimator |
Method Summary | |
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void |
addData(double[] y)
Adds sample data |
protected abstract double |
calculateIC(int p,
int t,
double var)
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protected double |
calculateYVariance(int p)
Calculates the variance on the sample for a given lag order |
protected static MultipleLinearRegressionEstimator |
createDefaultRegressionEstimator()
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double |
estimateIC(int p)
Estimates the IC value for a given lag order |
protected int |
getSampleSize()
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int |
minimiseIC(int minLag,
int maxLag)
Minimise the IC value for a given lag order interval |
protected double[] |
toRegressands(double[] y,
int p)
Converts sample to regression regressand |
protected double[][] |
toRegressors(double[] y,
int p)
Converts sample to regression regressors |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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protected AbstractInformationCriterionEstimator()
protected AbstractInformationCriterionEstimator(MultipleLinearRegressionEstimator regression)
regression
- the GeneralLinearRegressionMethod Detail |
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public void addData(double[] y)
InformationCriterionEstimator
addData
in interface InformationCriterionEstimator
y
- the array of size T representing the y samplepublic int minimiseIC(int minLag, int maxLag)
InformationCriterionEstimator
minimiseIC
in interface InformationCriterionEstimator
minLag
- the minimum lag order usedmaxLag
- the maximum lag order used
protected int getSampleSize()
protected double calculateYVariance(int p)
p
- the lag order
protected double[] toRegressands(double[] y, int p)
y
- the samplep
- the lag order
protected double[][] toRegressors(double[] y, int p)
y
- the samplep
- the lag order
public double estimateIC(int p)
InformationCriterionEstimator
estimateIC
in interface InformationCriterionEstimator
p
- the lag order used
protected abstract double calculateIC(int p, int t, double var)
protected static MultipleLinearRegressionEstimator createDefaultRegressionEstimator()
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