On normal approximation for strongly mixing random variables

DOI10.1007/s10440-007-9122-1zbMath1118.60017OpenAlexW2074432745MaRDI QIDQ996763

Jonas Kazys Sunklodas

Publication date: 19 July 2007

Published in: Acta Applicandae Mathematicae (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10440-007-9122-1

zbMATH Keywords

Stein's methodnormal approximationbounded Lipschitz metricstrong mixing conditionweakly dependent random variables

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05)

Related Items

On normal approximation of discounted and strongly mixing random variables ⋮ Sunklodas' approach to normal approximation for time-dependent dynamical systems ⋮ On normal approximation for \(\varphi\)-mixing and \(m\)-dependent random variables ⋮ Some estimates of the normal approximation for \(\varphi \)-mixing random variables ⋮ Nonparametric link prediction in large scale dynamic networks ⋮ On the discounted global CLT for some weakly dependent random variables ⋮ A Berry-Esseen theorem for sample quantiles under weak dependence

Cites Work

• Estimate of the bounded Lipschitz metric for sums of weakly dependent random variables
• \(L_1\) bounds for asymptotic normality of m-dependent sums using Stein's technique
• A new method for approximations in probability and operator theories
• About the Berry-Esseen theorem for weakly dependent sequences
• On the Convergence Rate in the Central Limit Theorem for Weakly Dependent Random Variables
• An Introduction to Stein's Method
• Multiple comparisons and sums of dissociated random variables
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