Central limit theorem for linear processes with infinite variance

DOI10.1007/s10959-011-0393-0zbMath1296.60062arXiv1105.6129OpenAlexW2015728592MaRDI QIDQ742110

Hailin Sang, Magda Peligrad

Publication date: 18 September 2014

Published in: Journal of Theoretical Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1105.6129

zbMATH Keywords

linear processcentral limit theoremmartingalemixinginfinite variance

Mathematics Subject Classification ID

Martingales with discrete parameter (60G42) Central limit and other weak theorems (60F05) Stationary stochastic processes (60G10)

Related Items (10)

Central limit theorems for weighted sums of dependent random vectors in Hilbert spaces via the theory of the regular variation ⋮ Self-normalized limit theorems for linear processes generated by \(\rho\)-mixing innovations ⋮ Functional convergence of linear processes with heavy-tailed innovations ⋮ Limit theorems for linear random fields with innovations in the domain of attraction of a stable law ⋮ Central limit theorems for moving average processes ⋮ Remarks on limit theorems for reversible Markov processes and their applications ⋮ Asymptotics for a class of dependent random variables ⋮ Asymptotic Properties for Linear Processes of Functionals of Reversible or Normal Markov Chains ⋮ Convergence in mean and central limit theorems for weighted sums of martingale difference random vectors with infinite rth moments ⋮ The Self-normalized Asymptotic Results for Linear Processes

Uses Software

• longmemo

Cites Work

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