  It is shown that any linear estimator that satisfies the moment conditions up
to order $p$ is equivalent to a local polynomial regression of order $p$ with
some non-negative weight function if and only if the kernel has at most $p$
sign changes. If the data points are placed symmetrically about the estimation
point, a linear weighting function is equivalent to the standard quadratic
weighting function.
