Let {X <Subscript> n </Subscript>,n≥1} be a strictly stationary sequence of negatively associated random variables with the marginal probability density function f(x), the recursive kernel estimate of f(x) is defined by [InlineMediaObject not available: see fulltext.] where h <Subscript> n </Subscript> is a sequence of positive bandwidths...</subscript></subscript>

Suppose the observations (X <Subscript> i </Subscript>, Y <Subscript> i </Subscript>) taking values in R <Superscript> d </Superscript>×R, [InlineMediaObject not available: see fulltext.] are φ-mixing. Compared with the i.i.d. case, some known strong uniform convergence results for the estimators of the regression function r(x)=E(Y <Subscript> i </Subscript>|X <Subscript> i </Subscript>=x) need strong moment...</subscript></subscript></superscript></subscript></subscript>