The Annals of Probability

Stable Limits for Partial Sums of Dependent Random Variables

Richard A. Davis

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Abstract

Let $\{X_n\}$ be a stationary sequence of random variables whose marginal distribution $F$ belongs to a stable domain of attraction with index $\alpha, 0 < \alpha < 2$. Under the mixing and dependence conditions commonly used in extreme value theory for stationary sequences, nonnormal stable limits are established for the normalized partial sums. The method of proof relies heavily on a recent paper by LePage, Woodroofe, and Zinn which makes the relationship between the asymptotic behavior of extreme values and partial sums exceedingly clear. Also, an example of a process which is an instantaneous function of a stationary Gaussian process with covariance function $r_n$ behaving like $r_n \log n \rightarrow 0$ as $n \rightarrow \infty$ is shown to satisfy these conditions.

Article information

Source
Ann. Probab., Volume 11, Number 2 (1983), 262-269.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aop/1176993595

Digital Object Identifier
doi:10.1214/aop/1176993595

Mathematical Reviews number (MathSciNet)
MR690127

Zentralblatt MATH identifier
0511.60021

JSTOR
links.jstor.org

Subjects
Primary: 60F05: Central limit and other weak theorems
Secondary: 60G10: Stationary processes 60G15: Gaussian processes

Keywords
Stable distributions extreme values mixing conditions Gaussian processes

Citation

Davis, Richard A. Stable Limits for Partial Sums of Dependent Random Variables. Ann. Probab. 11 (1983), no. 2, 262--269. doi:10.1214/aop/1176993595. https://projecteuclid.org/euclid.aop/1176993595


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