# AIC/BIC formula wrong in James/Witten?

Reading "An Introduction to Statistical Learning" (by James, Witten, Hastie and Tibshirani), on p.211 I came across the following formula for BIC in case of linear regression:

$$BIC = \frac{1}{n \hat{\sigma}^2} \left[ RSS + (\log{n}) d \hat{\sigma}^2 \right]$$

up to a constant, and similarly for AIC. Here, $$n$$ is sample size and $$d$$ is the number of parameters. This seems contrary to the more popular formulation, where

$$BIC = n \log(\hat{\sigma}^2) + d \log{n}$$

The most obvious difference is that RSS in the first formula is not logged. Is the first formula wrong or am I missing something?

There is no error but there is a subtlety. Note: In the second edition of ISLR model selection is discussed on pages 232-235 .

Let's start by deriving the log-likelihood for linear regression as it's at the heart of this question.

The likelihood is a product of Normal densities. Evaluated at the MLE: $$\hat{L} = \prod_{i=1}^n\frac{1}{\sqrt{2\pi\hat{\sigma}^2}}\exp\left\{-\frac{(y_i - \hat{y}_i)^2}{2\hat{\sigma}^2}\right\}$$ where n is the number of data points and $$\hat{y}_i$$ is the prediction, so $$y_i - \hat{y}_i$$ is the residual.

We take the log and keep track of constants as they are important later on. $$\log(\hat{L}) = -\frac{n}{2}\log(2\pi\hat{\sigma}^2) - \sum_{i=1}^n \frac{-(y_i - \hat{y}_i)^2}{2\hat{\sigma}^2} = -\frac{n}{2}\log(2\pi\hat{\sigma}^2) - \frac{RSS}{2\hat{\sigma}^2}$$ where RSS is the residual sum of squares.

What about the MLE $$\hat{\sigma}^2$$ of the error variance $$\sigma^2$$? It's also a function of the RSS.

$$\hat{\sigma}^2 = \frac{RSS}{n}$$

And here is the subtle point. For model selection with AIC and BIC ISLR uses the $$\hat{\sigma}^2$$ from the full model to compare all nested models. Let's call this residual variance $$\hat{\sigma}^2_{full}$$ for clarity.

Finally we write down the Bayesian information criterion (BIC). d is the number of fixed effects.

$$BIC = -2 \log(\hat{L}) + \log(n)d = n\log(2\pi\hat{\sigma}^2_{full}) + \frac{RSS}{\hat{\sigma}^2_{full}} + \log(n)d \\ = c_0 + c_1\left(RSS + \log(n)d\hat{\sigma}^2_{full}\right)$$

This is Equation (6.3) in ISLR up to two constants, $$c_0$$ and $$c_1=\hat{\sigma}^{-2}_{full}$$, that are the same for all models under consideration. ISLR also divides BIC by the sample size n.

What if we want to estimate $$\sigma^2$$ separately for each model? Then we plug in the MLE $$\hat{\sigma}^2$$ = RSS/n and we get the "more popular" formulation. We add 1 to the number of parameters because we estimate the error variance plus the d fixed effects.

$$BIC = n\log(2\pi\hat{\sigma}^2) + \frac{RSS}{\hat{\sigma}^2} + \log(n)(d+1)\\ = n\log(2\pi RSS/n) + \frac{RSS}{RSS/n} + \log(n)(d+1)\\ = c^*_0 + n\log(RSS) + \log(n)(d+1)$$

The residual sum of squares RSS is the same in both versions of the BIC. [Since the effect estimates are $$\hat{\beta} = (X'X)^{-1}X'Y$$ and the predictions $$X(X'X)^{-1}X'Y$$ don't depend on $$\sigma^2$$.]

 G. James, D. Witten, T. Hastie, and R. Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer, 2nd edition, 2021. Available online.

• Thank you a lot for the detailed answer! To summarize the main point for the quick reader: $\sigma^2\approx \hat{\sigma}^2=RSS/n$ is inserted in the formua for the log likelihood of the model. Then we have the term $0.5 n \ln (\sigma)= 0.5 n \ln(RSS/n)$ left and the term $\frac{1}{\sigma^2}\sum_{i=1}^n (x_i-\mu)^2=n$. Therefore a term $ln(RSS)$remains. Jul 28 at 9:03
• The error variance $\sigma^2$ is a parameter just like the $\beta$s. We can either: (a) assume $\sigma^2$ is known and plug in a specific value, or (b) estimate $\sigma^2$ simultaneously with the mean structure parameters, the $\beta$s. The likelihood (and hence the AIC) is different in cases (a) and (b). Jul 28 at 11:27