Let be a complete separable metric space and (Fn)n[greater-or-equal, slanted]0 a sequence of i.i.d. random functions from to which are uniform Lipschitz, that is, Ln=supx[not equal to]y d(Fn(x),Fn(y))/d(x,y)[infinity] a.s. Providing the mean contraction assumption and for some , it was proved by...

Lam and Lehoczky (1991) have recently given a number of extensions of classical renewal theorems to superpositions of p independent renewal processes. In this article we want to advertise an approach that more explicitly uses a Markov renewal theoretic framework and thus leads to a simplified...

Let (S, £) be a measurable space with countably generated [sigma]-field £ and (Mn, Xn)n[greater-or-equal, slanted]0 a Markov chain with state space S x and transition kernel :S x ( [circle times operator] )--[0, 1]. Then (Mn,Sn)n[greater-or-equal, slanted]0, where Sn = X0+...+Xn for...

This article continues work by Alsmeyer and Hoefs (Markov Process Relat. Fields 7 (2001) 325-348) on random walks (Sn)n[greater-or-equal, slanted]0 whose increments Xn are (m+1)-block factors of the form [phi](Yn-m,...,Yn) for i.i.d. random variables Y-m,Y-m+1,... taking values in an arbitrary...

In this paper we extend well-known results by Baum and Katz (1965) and others on the rate of convergence in the law of large numbers for sums of i.i.d. random variables to general zero-mean martingales S. For , p1/[alpha] and f(x) = x (two-sided case) OR = x+ or x- (one-sided case), it is e.g....

Let be a stochastic process adapted to the filtration and with increments X1, X2, ... Set and Ln = m1 + ... + mn for n [greater-or-equal, slanted] 1. Then we call a linear growth process (LGP) if 1. (1) [mu] [less-than-or-equals, slant] Ln/n [less-than-or-equals, slant] [nu] a.s.f.a. n...

Precise asymptotics have been proved for sums like [summation operator]n=1[infinity]nr/p-2P(Sn[greater-or-equal, slanted][var epsilon]n1/p) as [var epsilon][downward right arrow]0, where {Sn, n[greater-or-equal, slanted]1} are partial sums i.i.d. random variables, and, more recently, for renewal...

Let Sn,n = 1, 2, ..., denote the partial sums of integrable random variables. No assumptions about independence are made. Conditions for the finiteness of the moments of the first passage times N(c) = min {n: Snca(n)}, where c = 0 and a(y) is a positive continuous function on [0, [infinity]),...

Let X1, X2,... be independent random variables with a common continuous distribution function. Rates of convergence in limit theorems for record times and the associated counting process are established. The proofs are based on inversion, a representation due to Williams and random walk methods.