The Annals of Probability

Asymptotic Independence in the Multivariate Central Limit Theorem

William N. Hudson and Howard G. Tucker

Full-text: Open access

Abstract

Necessary and sufficient conditions are given for asymptotic independence in the multivariate central limit theorem. If $\{X_n\}$ is a sequence of independent, identically distributed random variables whose common distribution is symmetric, and if the distribution of $X^2_1$ is in the domain of attraction of a stable distribution of characteristic exponent $\alpha$, then $\bar{X}$ and $s^2$ are asymptotically independent if and only if $1 \leqslant \alpha \leqslant 2$. If the components of a multivariate infinitely divisible distribution are pairwise independent, then they are independent.

Article information

Source
Ann. Probab., Volume 7, Number 4 (1979), 662-671.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aop/1176994989

Digital Object Identifier
doi:10.1214/aop/1176994989

Mathematical Reviews number (MathSciNet)
MR537213

Zentralblatt MATH identifier
0411.60028

JSTOR
links.jstor.org

Subjects
Primary: 60F05: Central limit and other weak theorems

Keywords
Multivariate central limit theorem asymptotic independence stable distributions and their domains of attraction

Citation

Hudson, William N.; Tucker, Howard G. Asymptotic Independence in the Multivariate Central Limit Theorem. Ann. Probab. 7 (1979), no. 4, 662--671. doi:10.1214/aop/1176994989. https://projecteuclid.org/euclid.aop/1176994989


Export citation