On normal approximation of discounted and strongly mixing random variables

DOI10.1007/s10986-007-0022-xzbMath1266.60045OpenAlexW2049381378MaRDI QIDQ2376371

Jonas Kazys Sunklodas

Publication date: 21 June 2013

Published in: Lithuanian Mathematical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10986-007-0022-x

zbMATH Keywords

Stein's methodLipschitz conditionstrong mixing conditiondiscounted central limit theorem

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05)

Related Items

Uniform bound for asymptotic normality of discounted \(m\)-dependent random variables using Heinrich's method

Cites Work

• Estimate of the bounded Lipschitz metric for sums of weakly dependent random variables
• On normal approximation for strongly mixing 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
• Moving averages with random coefficients and random coefficient autoregressive models
• Multiple comparisons and sums of dissociated random variables
• The Tail Probability of Discounted Sums of Pareto-like Losses in Insurance
• The Discounted Central Limit Theorem and its Berry-Esseen Analogue
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