On the Komlos, Major and Tusnady strong approximation for some classes of random iterates

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

卷:128

Article

Cuny, Christophe¹  Dedecker, Jerome²  Merlevede, Florence³ 

[1] Univ Nouvelle Caledonie, Inst Sci Exactes & Appl, Noumea, France

[2] Univ Paris 05, Sorbonne Paris Cite, Lab MAPS, UMR 8145, Paris, France

[3] Univ Paris Est, CNRS, UPEM, LAMA UMR 8050,UPEC, Champs Sur Marne, France

关键词: Strong invariance principle;    KMT approximation;    Random iterates;    Markov chains;    Left random walk on GL(d)(R);   

DOI  :  10.1016/j.spa.2017.07.011

来源: Elsevier

【 摘 要 】

The famous results of Komlos, Major and Tusnady (see Komlos et al., 1976 [15] and Major, 1976 [17]) state that it is possible to approximate almost surely the partial sums of size of i.i.d. centered random variables in L-p (p > 2) by a Wiener process with an error term of order o(n(1/p)). Very recently, Berkes et al. (2014) extended this famous result to partial sums associated with functions of an i.i.d. sequence, provided a condition on a functional dependence measure in L-p is satisfied. In this paper, we adapt the method of Berkes, Liu and Wu to partial sums of functions of random iterates. Taking advantage of the Markovian setting, we shall give new dependent conditions, expressed in terms of a natural coupling (in L-infinity or in L-1), under which the strong approximation result holds with rate o(n(1/p)) . As we shall see our conditions are well adapted to a large variety of models, including left random walks on GL(d)(R), contracting iterated random functions, autoregressive Lipschitz processes, and some ergodic Markov chains. We also provide some examples showing that our L-1-coupling condition is in some sense optimal. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free