  We apply the stochastic approximation method to construct a large class of
recursive kernel estimators of a probability density, including the one
introduced by Hall and Patil (1994). We study the properties of these
estimators and compare them with Rosenblatt's nonrecursive estimator. It turns
out that, for pointwise estimation, it is preferable to use the nonrecursive
Rosenblatt's kernel estimator rather than any recursive estimator. A contrario,
for estimation by confidence intervals, it is better to use a recursive
estimator rather than Rosenblatt's estimator.
