The Annals of Probability

Functional Limit Theorems for Dependent Variables

Richard Durrett and Sidney I. Resnick

Full-text: Open access

Abstract

Conditions are given for a sequence of stochastic processes derived from row sums of an array of dependent random variables to converge to a process with stationary, independent increments or to a process with continuous paths. We also discuss when row maxima converge to an extremal process. The first result is a generalization of the classical results for independent random variables. The second result gives general conditions for convergence to processes which can be obtained from Brownian motion by a random change of time. This result is used to give a unified development of most of the martingale central limit theorems in the literature. An important aspect of our methods is that after the initial result is shown, we can avoid any further consideration of tightness.

Article information

Source
Ann. Probab., Volume 6, Number 5 (1978), 829-846.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aop/1176995431

Digital Object Identifier
doi:10.1214/aop/1176995431

Mathematical Reviews number (MathSciNet)
MR503954

Zentralblatt MATH identifier
0398.60024

JSTOR
links.jstor.org

Subjects
Primary: 60F05: Central limit and other weak theorems
Secondary: 60G45 60J30

Keywords
Weak convergence invariance principle Poisson process Brownian motion process with stationary independent increments extremal process martinglaes

Citation

Durrett, Richard; Resnick, Sidney I. Functional Limit Theorems for Dependent Variables. Ann. Probab. 6 (1978), no. 5, 829--846. doi:10.1214/aop/1176995431. https://projecteuclid.org/euclid.aop/1176995431


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