Unformatted text preview: Akaike Information Criterion AIC.
ˆ
AIC = −2l (θ; Y ) + 2p
where p is the number of parameters in the statistical
model.
Select model with minimum AIC . UNM For our example,
AIC0 = 2(68.3868) + 2(1) = 138.7736
AIC1 = 2(67.0230) + 2(2) = 138.046
So the preferred model is θ1 = θ2 but barely.
AIC rewards goodness of ﬁt or models with large likelihood
Includes penalty that increases with number of parameters.
Attempts to avoid overﬁtting.
Different penalties provide different criteria,
ˆ
BIC =...
View
Full Document
 Fall '13
 GabrielHuerta
 Trigraph, Maximum likelihood, Errors and residuals in statistics, Akaike information criterion

Click to edit the document details