Complete convergence and strong law of large numbers for arrays of random variables under sublinear expectations
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Publication:5078070
DOI10.1080/03610926.2019.1625924OpenAlexW2951164844MaRDI QIDQ5078070
Publication date: 20 May 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2019.1625924
complete convergencestrong law of large numberssublinear expectationarray of random variableswidely negative dependence
Related Items (6)
Complete and complete integral convergence for arrays of row wise widely negative dependent random variables under the sub-linear expectations ⋮ Complete integral convergence for weighted sums of widely negative dependent random variables under the sub-linear expectations ⋮ Central limit theorem for linear processes generated by m-dependent random variables under the sub-linear expectation ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Complete and complete integral convergence for weighted sums of arrays of rowwise widely negative dependent random variables under the sub-linear expectations
Cites Work
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- Strong laws of large numbers for sub-linear expectations
- A note on the complete convergence for arrays of dependent random variables
- Function spaces and capacity related to a sublinear expectation: application to \(G\)-Brownian motion paths
- Expected utility with purely subjective non-additive probabilities
- A note on complete convergence for arrays
- A strong law of large numbers for capacities
- Complete convergence theorems for extended negatively dependent random variables
- A strong law of large numbers for non-additive probabilities
- More on complete convergence for arrays
- On the Strong Rates of Convergence for Arrays of Rowwise Negatively Dependent Random Variables
- Ambiguity, Risk, and Asset Returns in Continuous Time
- Complete Convergence and the Law of Large Numbers
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