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Concentration inequality and the weak law of large numbers for the sum of partly negatively dependent φ-subgaussian random variables

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  • Tanoue, Yuta

Abstract

We obtained the inequality of the φ-subgaussian standard, concentration inequality for the sum, and weak law of large numbers for partly negatively dependent φ-subgaussian random variables. Furthermore, results for m-acceptable φ-subgaussian random variables were obtained.

Suggested Citation

  • Tanoue, Yuta, 2024. "Concentration inequality and the weak law of large numbers for the sum of partly negatively dependent φ-subgaussian random variables," Statistics & Probability Letters, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:stapro:v:206:y:2024:i:c:s0167715223002031
    DOI: 10.1016/j.spl.2023.109979
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    References listed on IDEAS

    as
    1. Tangpi, Ludovic, 2019. "Concentration of dynamic risk measures in a Brownian filtration," Stochastic Processes and their Applications, Elsevier, vol. 129(5), pages 1477-1491.
    2. Daniel Lacker, 2018. "Liquidity, Risk Measures, and Concentration of Measure," Mathematics of Operations Research, INFORMS, vol. 43(3), pages 813-837, August.
    3. Huiming Zhang & Haoyu Wei, 2022. "Sharper Sub-Weibull Concentrations," Mathematics, MDPI, vol. 10(13), pages 1-29, June.
    Full references (including those not matched with items on IDEAS)

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