Strong laws for weighted sums of \(m\)-extended negatively dependent random variables and its applications
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Publication:2227560
DOI10.1016/j.jmaa.2020.124566zbMath1476.60064OpenAlexW3087239062MaRDI QIDQ2227560
Publication date: 15 February 2021
Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmaa.2020.124566
complete convergencestrong law of large numbersstrong consistencymultiple linear regression model\(m\)-extended negatively dependent random variablesconcept of extended negatively dependent random variablesconditional value-at-risk estimatorlimiting behavior for weighted sumsmultivariate copula functionnegatively orthant dependentquasi-renewal counting process
Related Items (11)
Laws of large numbers under model uncertainty with an application to \(m\)-dependent random variables ⋮ Convergence rate for weighted sums of ψ-mixing random variables and applications ⋮ Consistency of nearest neighbor estimator of density function for \(m\)-END samples ⋮ Complete convergence and complete moment convergence for maximal weighted sums of arrays of rowwise extended negatively dependent random variables with statistical applications ⋮ Marcinkiewicz–Zygmund type strong law of large numbers for weighted sums of random variables with infinite moment and its applications ⋮ Complete moment convergence for maximum of randomly weighted sums of martingale difference sequences ⋮ The Hsu-Robbins-Erdös theorem for the maximum partial sums of quadruplewise independent random variables ⋮ Complete convergence and complete moment convergence for weighted sums of m-extended negatively dependent random variables ⋮ Almost sure summability of the maximal normed partial sums of \(m\)-dependent random elements in Banach spaces ⋮ Complete moment convergence for randomly weighted sums of arrays of rowwise \(m_n\)-extended negatively dependent random variables and its applications ⋮ The Lai law for weighted sums
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