Institute of Mathematical Statistics Lecture Notes - Monograph Series

Strong invariance principle for dependent random fields

Alexander Bulinski, Alexey Shashkin

Abstract

A strong invariance principle is established for random fields which satisfy dependence conditions more general than positive or negative association. We use the approach of Csörgő and Révész applied recently by Balan to associated random fields. The key step in our proof combines new moment and maximal inequalities, established by the authors for partial sums of multiindexed random variables, with the estimate of the convergence rate in the CLT for random fields under consideration.

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Primary Subjects: 60F15, 60F17
Keywords: dependent random fields; weak dependence; association; covariance inequalities; strong invariance principle; law of the iterated logarithm
Full-text: Open access
Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.lnms/1196285815
Mathematical Reviews (MathSciNet): MR2306195
Digital Object Identifier: doi:10.1214/074921706000000167

2012 © Institute of Mathematical Statistics

Institute of Mathematical Statistics Lecture Notes - Monograph Series

Institute of Mathematical Statistics Lecture Notes - Monograph Series