An empirical process central limit theorem for dependent non-identically distributed random variables

DOI10.1016/0047-259X(91)90039-5zbMath0732.60026MaRDI QIDQ808514

Donald W. K. Andrews

Publication date: 1991

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

nonlinear time series modelstrongly mixing sequencescentral limit theorem for empirical processes

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Functional limit theorems; invariance principles (60F17)

Related Items (9)

Quasi-Likelihood Estimation of a Censored Autoregressive Model With Exogenous Variables ⋮ Weak convergence of stochastic processes indexed by smooth functions ⋮ Evaluating forecast performance with state dependence ⋮ Penalized least squares estimation with weakly dependent data ⋮ Invariance principles for dependent processes indexed by Besov classes with an application to a Hausman test for linearity ⋮ SPATIAL SEMIPARAMETRIC MODEL WITH ENDOGENOUS REGRESSORS ⋮ \(S\)-estimation of nonlinear regression models with dependent and heterogeneous observations ⋮ Smoothed GMM for quantile models ⋮ Central limit theorems for empirical and \(U\)-processes of stationary mixing sequences

Uses Software

• nlmdl

Cites Work

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• A maximal inequality and dependent strong laws
• A nearly independent, but non-strong mixing, triangular array
• Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form
• Central Limit Theorems for dependent variables. I
• Limit theorems for sums of weakly dependent Banach space valued random variables
• Invariance principles for dependent variables
• Some Limit Theorems for Stationary Processes

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