Almost sure convergence of the multiple ergodic average for certain weakly mixing systems

The family of pairwise independently determined(PID) systems, i.e., those for which the independent joining is the only self joining with independent 2-marginals, is a class of systems for which the long standing open question by Rokhlin, of whether mixing implies mixing of all orders, has a positive answer. We show that in the class of weakly mixing PID one finds a positive answer for another long-standing open problem, whether the multiple ergodic averages 1/N N-1∑n=0 f_1(T~nx)···f_d(T~(dnx)),N→∞,almost surely converge.     

An almost sure central limit theorem for self-normalized products of sums of i.i.d. random variables

Let X,X₁,X₂,… be a sequence of independent and identically distributed positive random variables with EX=μ>0. In this paper we show that the almost sure central limit theorem for self-normalized products of sums holds only under the assumptions that X belongs to the domain of attraction of the normal law.     

Over-rotation numbers for unimodal maps

We introduce twist unimodal maps of the interval and describe their structure. Sufficient conditions for the growth of over-rotation interval in families of maps are given.     

Almost sure limit theorems for stable distributions

Consider a sequence of i.i.d. random variables in the domain of attraction of a stable distribution with an exponent in (0,2]. A universal result in almost sure limit theorem for the partial sums is established. Our results substantially extend and improve those on the almost sure central limit theorem previously obtained by Jonsson 2007, Berkes and Csáki 2001, and Hörmann 2007.     

Almost sure stability for uncertain differential equation

Uncertain differential equation is a type of differential equation driven by Liu process. So far, concepts of stability and stability in mean for uncertain differential equations have been proposed. This paper aims at providing a concept of almost sure stability for uncertain differential equation. A sufficient condition is given for an uncertain differential equation being almost surely stable, and some examples are given to illustrate the effectiveness of the sufficient condition.     

Almost sure central limit theorems for random fields

A general almost sure limit theorem is presented for random fields. It is applied to obtain almost sure versions of some (functional) central limit theorems. (© 2003 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Almost sure convergence of branching processes

A general theorem concerning the almost sure convergence of some nonhomogeneous Markov chains, whose conditional distributions satisfy a certain convergence condition, is given. This result applied to branching processes with infinite mean yields almost sure convergence for a large class of processes converging in distribution, as well as a characterization of the limiting distribution function.     

Almost sure invariance principles for lacunary trigonometric series

We prove almost sure invariance principles for lacunary trigonometric series. The result includes the series presented by P. Erdős.     

Precise rates in the law of logarithm for the moment convergence of i.i.d. random variables

Let be a sequence of i.i.d. random variables with EX=0 and EX²=σ²<∞. Set , Mn=maxk?n|Sk|, n?1. Let r>1, then we obtain

     

Decay of correlations for Fibonacci unimodal interval maps

We consider a class of generalized Fibonacci unimodal maps for which the central return times {s_n} satisfy that s_n = s_(n-1) +ks_(n-2) for some k ≥ 1. We show that such a unimodal map admits a unique absolutely continuous invariant probability with exactly stretched exponential decay of correlations if its critical order lies in(1, k+1).     

Almost sure convergence of sample range

Let X ₁, X ₂, ... be i.i.d. random variables. The sample range is R _n = max {X _i , 1 ≤ i ≤ n} − min {X _i , 1 ≤ i ≤ n}. If for a non-degenerate distribution G and some sequences (α _k ), (β _k ) then we have

Almost sure central limit theorems for random functions

Let {Xn,-∞< n <∞} be a sequence of independent identically distributed random variables with EX1 = 0, EX12 = 1 and let Sn =∑k=1∞Xk, and Tn = Tn(X1,…,Xn) be a random function such that Tn = ASn Rn, where supn E|Rn| <∞and Rn = o(n~(1/2)) a.s., or Rn = O(n1/2-2γ) a.s., 0 <γ< 1/8. In this paper, we prove the almost sure central limit theorem (ASCLT) and the function-typed almost sure central limit theorem (FASCLT) for the random function Tn. As a consequence, it can be shown that ASCLT and FASCLT also hold for U-statistics, Von-Mises statistics, linear processes, moving average processes, error variance estimates in linear models, power sums, product-limit estimators of a continuous distribution, product-limit estimators of a quantile function, etc.     

Topological and metrical conditions for Collet-Eckmann unimodal maps

1. IntroductionColletEchazann maps (i.e. every ColletEckmann maP has positive lower LyaPunovexponent at the critical value) play a very important role in the study of one dimensionaldynamical systems. They have very good metricaI and ergodic properties[ll2], for example,every COllet-Eckmann map admits an absolutely cootinuous invariani m.asur.[1].In I3,4] Benedicks and Carleson proved that Collet-Eckmann maps are abundant in theso-caJled quadratic family which is a typical family of one …     

Almost sure convergence of the Bartlett estimator

Summary We study the almost sure convergence of the Bartlett estimator for the asymptotic variance of the sample mean of a stationary weekly dependent process. We also study the a.\ s.\ behavior of this estimator in the case of long-range dependent observations. In the weakly dependent case, we establish conditions under which the estimator is strongly consistent. We also show that, after appropriate normalization, the estimator converges a.s. in the long-range dependent case as well. In both cases, our conditions involve fourth order cumulants and assumptions on the rate of growth of the truncation parameter appearing in the definition of the Bartlett estimator.     

Computing topological entropy for periodic sequences of unimodal maps

In this paper we introduce an algorithm which allows us to compute the topological entropy of a class of piecewise monotone continuous interval maps. The algorithm can be applied to a class of economic models called duopolies, and it can be useful to compute the topological entropy of periodic sequences of continuous maps which have been used in some population growth models.