The Annals of Probability

Large deviations and law of the iterated logarithm for partial sums normalized by the largest absolute observation

Lajos Horváth and Qi-Man Shao

Full-text: Open access

Abstract

Let ${X_n, 1 \leq n < \infty}$ be a sequence of independent identically distributed random variables in the domain of attraction of a stable law with index $0 < \alpha < 2$. The limit of $x_n^{-1}\log P{S_n/ \max |X_i| \geq x_n}$ is found when $x_n \to \infty$ and $\x_n/n \to 0$. The large deviation result is used to prove the law of the iterated logarithm for the self-normalized partial sums.

Article information

Source
Ann. Probab., Volume 24, Number 3 (1996), 1368-1387.

Dates
First available in Project Euclid: 9 October 2003

Permanent link to this document
https://projecteuclid.org/euclid.aop/1065725185

Digital Object Identifier
doi:10.1214/aop/1065725185

Mathematical Reviews number (MathSciNet)
MR1411498

Zentralblatt MATH identifier
0869.60025

Subjects
Primary: 60F10: Large deviations 60F15: Strong theorems
Secondary: 60G50: Sums of independent random variables; random walks 60G18: Self-similar processes

Keywords
Stable law domain of attraction large deviation law of the iterated logarithm self-normalized partial sums largest absolute observation

Citation

Horváth, Lajos; Shao, Qi-Man. Large deviations and law of the iterated logarithm for partial sums normalized by the largest absolute observation. Ann. Probab. 24 (1996), no. 3, 1368--1387. doi:10.1214/aop/1065725185. https://projecteuclid.org/euclid.aop/1065725185


Export citation