= Random-design nonparametric regression
{title2=$Y_i=m(X_i)+\varepsilon_i,\quad\mathbb E[\varepsilon_i\mid X_i]=0$}
The predictor values are random, and the <regression function> is the conditional mean of the response. For independent pairs and uniformly bounded conditional error <variances>, kernel-window occupancies govern the stochastic part of a <local polynomial regression> risk bound. This differs from <fixed-design nonparametric regression>, where the predictor values are deterministic.
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