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Law of the logarithm for weighted sums of negatively dependent random variables under sublinear expectation

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  • Feng, Xinwei

Abstract

In this paper, with the definition of sequence of negatively dependent random variables under sublinear expectation, we establish law of the logarithm for weighted sums of this kind of sequence under sublinear expectation. This is the natural extension of classical limit theorem to the case that the probability is no longer additive.

Suggested Citation

  • Feng, Xinwei, 2019. "Law of the logarithm for weighted sums of negatively dependent random variables under sublinear expectation," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 132-141.
    • Handle: RePEc:eee:stapro:v:149:y:2019:i:c:p:132-141
    DOI: 10.1016/j.spl.2019.01.033
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    References listed on IDEAS

    as
    1. Li, D. L. & Rao, M. B. & Wang, X. C., 1995. "On the Strong Law of Large Numbers and the Law of the Logarithm for Weighted Sums of Independent Random Variables with Multidimensional Indices," Journal of Multivariate Analysis, Elsevier, vol. 52(2), pages 181-198, February.
    2. Zengjing Chen & Feng Hu, 2014. "A law of the iterated logarithm under sublinear expectations," Journal of Financial Engineering (JFE), World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 1-23.
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