An empirical process central limit theorem for dependent non-identically distributed random variables
type: Item
instance of: scholarly article
title: An empirical process central limit theorem for dependent non-identically distributed random variables
zbMATH Open document ID: 0732.60026
DOI: 10.1016/0047-259X(91)90039-5
author: Donald W. K. Andrews
published in: Journal of Multivariate Analysis
publication date: 1991-01-01T00:00:00Z
review text: The author proves a central limit theorem for empirical processes indexed by functions in the weakly dependent case. The underlying observations have to be near epoch dependent on strongly mixing random variables, a condition satisfied by functions of strongly mixing sequences. The class of functions has to satisfy some smoothness condition, allowing a series expansion with uniformly summable coefficients. In detail functions on a bounded subset of \(R^ k\) with a uniformly bounded Sobolev norm are treated. As an example the author studies the asymptotic normality of a weighted least squares estimator in a nonlinear time series model.
Mathematics Subject Classification ID: 60F05
Mathematics Subject Classification ID: 60F17
zbMATH DE Number: 4211158
zbMATH Keywords: central limit theorem for empirical processes
zbMATH Keywords: strongly mixing sequences
zbMATH Keywords: nonlinear time series model
reviewed by: Herold Dehling
describes a project that uses: nlmdl
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 4211158