Rates of strong uniform consistency for multivariate kernel density estimators

Let f_n denote the usual kernel density estimator in several dimensions. It is shown that if {a_n} is a regular band sequence, K is a bounded square integrable kernel of several variables, satisfying some additional mild conditions ((K₁) below), and if the data consist of an i.i.d. sample from a distribution possessing a bounded density f with respect to Lebesgue measure on R^d, then for some absolute constant C that depends only on d. With some additional but still weak conditions, it is proved that the above sequence of normalized suprema converges a.s. to . Convergence of the moment generating functions is also proved. Neither of these results require f to be strictly positive. These results improve upon, and extend to several dimensions, results by Silverman 13] for univariate densities.