The Annals of Probability

Asymptotic Normality, Strong Mixing and Spectral Density Estimates

M. Rosenblatt

Full-text: Open access

Abstract

Asymptotic normality is proven for spectral density estimates assuming strong mixing and a limited number of moment conditions for the process analyzed. The result holds for a large class of processes that are not linear and does not require the existence of all moments.

Article information

Source
Ann. Probab., Volume 12, Number 4 (1984), 1167-1180.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aop/1176993146

Digital Object Identifier
doi:10.1214/aop/1176993146

Mathematical Reviews number (MathSciNet)
MR757774

Zentralblatt MATH identifier
0545.62058

JSTOR
links.jstor.org

Subjects
Primary: 60F05: Central limit and other weak theorems
Secondary: 62M15: Spectral analysis

Keywords
Asymptotic normality strong mixing spectral density estimates cumulants nonlinear functions of Gaussian processes

Citation

Rosenblatt, M. Asymptotic Normality, Strong Mixing and Spectral Density Estimates. Ann. Probab. 12 (1984), no. 4, 1167--1180. doi:10.1214/aop/1176993146. https://projecteuclid.org/euclid.aop/1176993146


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