I'm reading about resampling methods, and specifically leave-one-out cross-validation.
I understood the method, and how to calculate the estimate of the test MSE (Mean squared error):
In the setup of linear regression we have:
$h_i$ is the leverage of the $i$th observation. I want to know the utility of dividing by $1-h_i$. And why not just use the normal formula of MSE?