Let s{;Xns};, n [greater-or-equal, slanted] 1, be a stationary [alpha]-mixing sequence of real-valued r.v.'s with distribution function (d.f.) F, probability density function (p.d.f.) f and mixing coefficient [alpha](n). The d.f. F is estimated by the empirical d.f. Fn, based on the segment...

Let X1,X2,... be real-valued random variables forming a strictly stationary sequence, and satisfying the basic requirement of being positively or negatively associated. Let [xi]p denote the pth quantile of the marginal distribution function of the Xi's, which is estimated by a smooth...

The sole purpose of this paper is to establish asymptotic normality of the usual kernel estimate of the marginal probability density function of a strictly stationary sequence of associated random variables. In much of the discussions and derivations, the term association is used to include both...

Let Zd be the lattice of points in d with integer coordinates, and let {Xn}, n [epsilon] Zd, be a random field of real-valued translation invariant random variables with unknown distribution function F. For u and v in Zd with u < v let, Buv be the box in Zd defined by Buv = {n [epsilon] Zd; u < n [less-than-or-equals, slant] v}, and for N = 1, 2, ..., let k(N) = (k1 (N), ..., kd (N)) with ki(N) --> [infinity] as N -- [infinity], I = 1, ..., d. On the basis of the...</v>

Consider the fixed regression model with general weights, and suppose that the error random variables are coming from a strictly stationary stochastic process, satisfying the strong mixing condition. The asymptotic normality of the proposed estimate is established under weak conditions. The...

Let the random variables (r.v.'s) X0,X1,... be defined on the probability space and take values in , where S is a measurable subset of a Euclidean space and is the [sigma]-field of Borel subsets of S, and suppose that they form a general stochastic process. It is assumed that all finite...