# SuanShu, a Java numerical and statistical library

com.numericalmethod.suanshu.stats.regression.linear.residualanalysis

## Class LMInformationCriteria

• java.lang.Object
• com.numericalmethod.suanshu.stats.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.

• ### Constructor Summary

Constructors
Constructor and Description
LMInformationCriteria(LMResiduals residuals)
Computes the information criteria from residual analysis.
• ### Method Summary

All Methods
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