= Interior first-order bias cancellation for local constant regression
{title2=$\operatorname{Bias}=o(h)$}
At an interior point where the <regression function> is differentiable, a symmetric compactly supported <regression kernel> makes the first-order <bias of an estimator> vanish asymptotically. In regular <fixed-design nonparametric regression> with $h\to0$ and $nh\to\infty$, the <local constant estimator> then has squared <bias of an estimator> $o(h^2)$ and <variance> $R(K)/(nh)+o((nh)^{-1})$ for a unit-integral <regression kernel>. The <differentiability> remainder is pointwise and need not hold uniformly over a function class.
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