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Some strong limit theorems of weighted sums for negatively dependent generalized Gaussian random variables

Author

Listed:
  • Amini, M.
  • Zarei, H.
  • Bozorgnia, A.

Abstract

In this paper, we study strong convergence of weighted sums , where {Xn,n[greater-or-equal, slanted]1} is a sequence of negative dependence, generalized Gaussian random variables and ank, n[greater-or-equal, slanted]1, k[greater-or-equal, slanted]1 is an array of real numbers such that, for [beta]>0 and every n[greater-or-equal, slanted]1.

Suggested Citation

  • Amini, M. & Zarei, H. & Bozorgnia, A., 2007. "Some strong limit theorems of weighted sums for negatively dependent generalized Gaussian random variables," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1106-1110, June.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:11:p:1106-1110
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    Citations

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    Cited by:

    1. Xuejun Wang & Zeyu Si, 2015. "Complete consistency of the estimator of nonparametric regression model under ND sequence," Statistical Papers, Springer, vol. 56(3), pages 585-596, August.
    2. Boukhari, Fakhreddine & Malti, Dounyazed, 2018. "Convergence of series of strongly integrable random variables and applications," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 191-200.
    3. Soo Sung, 2012. "Complete convergence for weighted sums of negatively dependent random variables," Statistical Papers, Springer, vol. 53(1), pages 73-82, February.

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