org.codehaus.jet.regression.estimators
Class SchwarzInformationCriterionEstimator

java.lang.Object
  extended by org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
      extended by org.codehaus.jet.regression.estimators.SchwarzInformationCriterionEstimator
All Implemented Interfaces:
InformationCriterionEstimator

public class SchwarzInformationCriterionEstimator
extends AbstractInformationCriterionEstimator

Estimator for the Schwarz Information Criterion (SIC)

 SIC(p)= log(sigma^2(p)+[p*log(T)]/T
 

Author:
Mauro Talevi
See Also:
InformationCriterionEstimator

Constructor Summary
SchwarzInformationCriterionEstimator()
          Creates an SchwarzInformationCriterionEstimator with default regression estimator
SchwarzInformationCriterionEstimator(MultipleLinearRegressionEstimator regression)
          Creates an SchwarzInformationCriterionEstimator with given regression estimator
 
Method Summary
protected  double calculateIC(int p, int T, double var)
          Calculate SIC
 
Methods inherited from class org.codehaus.jet.regression.estimators.AbstractInformationCriterionEstimator
addData, calculateYVariance, createDefaultRegressionEstimator, estimateIC, getSampleSize, minimiseIC, toRegressands, toRegressors
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

SchwarzInformationCriterionEstimator

public SchwarzInformationCriterionEstimator()
Creates an SchwarzInformationCriterionEstimator with default regression estimator


SchwarzInformationCriterionEstimator

public SchwarzInformationCriterionEstimator(MultipleLinearRegressionEstimator regression)
Creates an SchwarzInformationCriterionEstimator with given regression estimator

Parameters:
regression - the MultipleLinearRegressionEstimator
Method Detail

calculateIC

protected double calculateIC(int p,
                             int T,
                             double var)
Calculate SIC
 SIC(p)= log(sigma^2(p)+[p*log(T)]/T
 

Specified by:
calculateIC in class AbstractInformationCriterionEstimator
Parameters:
p - the lag order
T - the sample size
var - the sample variance
Returns:
The SIC value


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