Weak laws of large numbers for maximal weighted sums of random variables

DOI10.1080/03610926.2019.1630437OpenAlexW2952248405WikidataQ127678114 ScholiaQ127678114MaRDI QIDQ5079023

Fakhreddine Boukhari

Publication date: 25 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2019.1630437

zbMATH Keywords

limit theoremsmaximal inequalitiesweighted averagesKolmogorov-Feller weak lawMarcinkiewicz-Zygmund weak law

Mathematics Subject Classification ID

Statistics (62-XX) Sums of independent random variables; random walks (60G50) Strong limit theorems (60F15)

Related Items (7)

A note on the weak law of large numbers for weighted negatively superadditive dependent random variables ⋮ Weak law of large numbers without any restriction on the dependence structure of random variables ⋮ Limit theorems for dependent random variables with infinite means ⋮ On a Feller–Jajte strong law of large numbers ⋮ A remark on the Kolmogorov-Feller weak law of large numbers ⋮ A lower bound for the tail probability of partial maxima of dependent random variables and applications ⋮ On a weak law of large numbers with regularly varying normalizing sequences

Cites Work

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• An extension of the Kolmogorov-Feller weak law of large numbers with an application to the St. Petersburg game
• Iterated logarithm laws for asymmetric random variables barely with or without finite mean
• Probability: A Graduate Course
• A version of the Kolmogrov–Feller weak law of large numbers for maximal weighted sums of random variables
• A Generalization of Weak Law of Large Numbers
• On the strong law of large numbers

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