I have two questions regarding the AIC criterion : AIC=$2k-2ln(l)$

Where does the number 2 comes from? As we usually minimize it why don't we consider only : $k-ln(l)$. (Maybe I am missing something.)

Second question : Can we consider that AIC selection follow the same "philosophy" as lasso/ridge selection ?

Indeed, if we consider that $0^{0}=0$ we would then have:

AIC selection: max[$ ln(l)-\lambda\sum|\beta|^{0}$] (with $\lambda=1$)

Lasso selection: max[$ ln(l)-\lambda\sum|\beta|^{1}$]

Ridge selection: max[$ ln(l)-\lambda\sum|\beta|^{2}$]


2 Answers 2


Of course, you get the same answer without the factor of 2. Burnham & Anderson refer to Akaike's multiplication by -2 as done for "historical reasons." I believe what they mean is the following. Historically, AIC was developed in the context of linear regression, which assumes errors are iid mean 0. Oneof the classic ways to fit such models was chi-square fitting. Twice the NLL happens to exactly equal the chi-square value (see https://en.wikipedia.org/wiki/Akaike_information_criterion#Chi-squared_fits). I believe that is why Akaike multiplied the loglikelihood by -2, so as to make it equivalent. Also, see section 4 of this paper.


The −2ln(l) is the deviance of a model, and it is useful to compare models of the same size (same number of variables). All the likelihood ratio tests is based in the comparison of the deviances between models, and the 2 is necessary for know the theoretical distribution of the statistic (chi-squared)

The deviance decreases always when you add one variable to the model and the 2k of the AIC is a penalization for evaluate if this decrement is small.

Then, the AIC is useful to compare models of different sizes. Deleting the 2 of the formulas, it is the same, but conceptually with the 2 you see the relationship between deviance and AIC.


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