What is the term $K$ in Akaike information criterion? The AIC is defined as $2K-2log(L)$, where $L$ is the maximized value of the likelihood function for the estimated model.
On the internet, I found three competing candidates:
- Number of parameters + error term (for simple linear one-predictor model, intercept, slope and error term: $K=3$)
- Number of parameters (for the linear one-predictor model, intercept and slope: $K=2$)
- Number of predictors (for the linear one-predictor model, the slope: $K=1$)
Which one is correct and why?