  We give analytic methods for nonparametric bias reduction that remove the
need for computationally intensive methods like the bootstrap and the
jackknife.
  We call an estimate {\it $p$th order} if its bias has magnitude $n_0^{-p}$ as
$n_0 \to \infty$, where $n_0$ is the sample size (or the minimum sample size if
the estimate is a function of more than one sample). Most estimates are only
first order and require O(N) calculations, where $N$ is the total sample size.
The usual bootstrap and jackknife estimates are second order but they are
computationally intensive, requiring $O(N^2)$ calculations for one sample. By
contrast Jaeckel's infinitesimal jackknife is an analytic second order one
sample estimate requiring only O(N) calculations. When $p$th order bootstrap
and jackknife estimates are available, they require $O(N^p)$ calculations, and
so become even more computationally intensive if one chooses $p>2$.
  For general $p$ we provide analytic $p$th order nonparametric estimates that
require only O(N) calculations. Our estimates are given in terms of the von
Mises derivatives of the functional being estimated, evaluated at the empirical
distribution.
  For products of moments an unbiased estimate exists: our form for this
"polykay" is much simpler than the usual form in terms of power sums.
