Leave-one-out cross-validation

Leave-one-out cross-validation performs one fit with each observation held out in turn and averages the held-out losses.

If a linear smoother has fitted vector $\hat{Y}=HY$, its residual after fitting without observation $i$ isThe identity follows from a block matrix inverse or a rank-one inverse update.