Let X1, X2,..., Xn be p-dimensional random vectors coming from a strictly stationary sequence which is also subject to any one of the four standard kinds of mixing. The survival function is defined by (x) = P(X x), where X is distributed as the X's above and the inequality X x is to be...

For n = 1, 2,... and i integer between 1 and n, let xni be fixed design points in a compact subset S of , and let Yni be observations taken at these points through g, an unknown continuous real-valued function defined on , and subject to errors [var epsilon]ni; that is, Yni = g(xni) + [var...

For j=1, 2,..., let {Zj}={(Xj, Yj)} be a strictly stationary sequence of random variables, where the X's and the Y's are 1p-valued and 1q-valued, respectively, for some integers p, q[greater-or-equal, slanted]1. Let [phi] be an integrable Borel real-valued function defined on 1q and set 97. The...

In this note, an upper bound is provided for the supremum of the absolute value of the difference of the probability density functions of two k-dimensional random vectors. The bound involves integrals of the absolute value of the characteristic functions of the random vectors, and shares a...

Sharp convergence rates for fixed design regression estimators for negatively associated random variables are established. The rates are obtained by means of exponential inequalities, which hold under general conditions.

Let X1,..., Xn + 1 be the first n + 1 random variables from a strictly stationary Markov process which satisfies certain additional regularity conditions. On the basis of these random variables, a recursive nonparametric estimate of the one-step transition distribution function is shown to be...

Let X1, X2,... be associated random variables forming a strictly stationary sequence, and let f be the probability density function of X1. For r [greater-or-equal, slanted] 0 integer, let f(r) be the rth order derivative of f. Under suitable regularity conditions on a kernel function K, a...