G-optimal design
= G-optimal design
{c}
{title2=$\min_\xi\sup_x d(x,\xi)$}
A G-optimal design minimizes the maximum <design sensitivity function> over the full design region. Thus it minimizes the worst <variance of a fitted regression mean>, excluding the separate noise <variance> of a new observation. The sensitivity average is $p$, so its supremum is at least $p$.