dev.nm.stat.regression.linear.residualanalysis

Class LMInformationCriteria

java.lang.Object

dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria

public class LMInformationCriteria
extends Object

The information criteria measure the goodness of fit of an estimated statistical model. The information criteria (IC) are tests between models - a tool for model selection. Given a data set, several competing models may be ranked according to their IC, with the one having the lowest IC being the best. From the IC value one may infer that e.g., the top three models are in a tie and the rest are far worse, but it would be arbitrary to assign a value above which a given model is 'rejected'.

Akaike's information criterion is a measure of the goodness of fit of an estimated statistical model. It is grounded in the concept of entropy, in effect offering a relative measure of the information lost when a given model is used to describe reality and can be said to describe the tradeoff between bias and variance in model construction, or loosely speaking that of accuracy and complexity of the model.

The BIC is very closely related to the Akaike information criterion (AIC). In BIC, the penalty for additional parameters is stronger than that of the AIC.

See Also:

• Wikipedia: Akaike information criterion
• Wikipedia: Bayesian information criterion

Constructor Summary

Constructors

Constructor and Description
LMInformationCriteria(LMResiduals residuals) Computes the information criteria from residual analysis.

Method Summary

Modifier and Type	Method and Description
double	AIC() Gets the Akaike information criterion.
double	BIC() Gets the Bayesian information criterion.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LMInformationCriteria

public LMInformationCriteria(LMResiduals residuals)

Computes the information criteria from residual analysis.

Parameters:

residuals - the residual analysis of a linear regression problem

Method Detail

AIC

public double AIC()

Gets the Akaike information criterion.

Returns:

the Akaike information criterion

See Also:

Wikipedia: Akaike information criterion

BIC

public double BIC()

Gets the Bayesian information criterion.

Returns:

the Bayesian information criterion

See Also:

Wikipedia: Bayesian information criterion