Results 11 to 20 of about 137 (132)
A note on the complete convergence for sequences of pairwise NQD random variables
Journal of Inequalities and Applications, 2011 In this paper, complete convergence and strong law of large numbers for sequences of pairwise negatively quadrant dependent (NQD) random variables with non-identically distributed are investigated.
Wu Qunying +3 more
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A note on the complete convergence for arrays of dependent random variables
Journal of Inequalities and Applications, 2011 A complete convergence result for an array of rowwise independent mean zero random variables was established by Kruglov et al. (2006). This result was partially extended to negatively associated and negatively dependent mean zero random variables by Chen
Sung Soo Hak
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Some strong limit theorems for arrays of rowwise negatively orthant-dependent random variables [PDF]
Journal of Inequalities and Applications, 2011 In this article, the strong limit theorems for arrays of rowwise negatively orthant-dependent random variables are studied. Some sufficient conditions for strong law of large numbers for an array of rowwise negatively orthant-dependent random variables ...
Shen Aiting
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On the exponential inequality for acceptable random variables
Journal of Inequalities and Applications, 2011 In this paper, we obtain some new exponential inequalities for partial sums and their finite maximum of acceptable random variables by the results of Sung et al. (J. Korean Stat. Soc., 40, 109-114, 2011) and in different ways from theirs.
Gao Qingwu, Wang Yuebao, Li Yawei
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Journal of Inequalities and Applications, 2011 In this article, some inequalities on convolution equations are presented firstly. The mean square stability of the zero solution of the impulsive stochastic Volterra equation is studied by using obtained inequalities on Liapunov function, including mean
Zhao Dianli, Han Dong
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Journal of the London Mathematical Society, Volume 103, Issue 4, Page 1618-1642, June 2021., 2021 Abstract We consider partial sums of a weighted Steinhaus random multiplicative function and view this as a model for the Riemann zeta function. We give a description of the tails and high moments of this object. Using these we determine the likely maximum of TlogT independently sampled copies of our sum and find that this is in agreement with a ...
Marco Aymone, Winston Heap, Jing Zhao
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Complete f-moment convergence for negatively superadditive dependent random variables [PDF]
, 2023 Mathematics subject classification (2020): 60F15.Copyright © the author(s). In this paper, by utilizing the Kolmogorov exponential type inequality of negatively superadditive dependent random arrays and truncated method, we study the complete f -moment ...
Xuep ng Hu +5 more
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On Chung‐Teicher type strong law for arrays of vector‐valued random variables
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 9, Page 443-458, 2004., 2004 We study the equivalence between the weak and strong laws of large numbers for arrays of row‐wise independent random elements with values in a Banach space ℬ. The conditions under which this equivalence holds are of the Chung or Chung‐Teicher types.
Anna Kuczmaszewska
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Complete convergence for arrays of minimal order statistics
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 44, Page 2325-2329, 2004., 2004 For arrays of independent Pareto random variables, this paper establishes complete convergence for weighted partial sums for the smaller order statistics within each row. This result improves on past strong laws. Moreover, it shows that we can obtain a finite nonzero limit for our normalized partial sums under complete convergence even though the first
André Adler
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On the order of growth of convergent series of independent random variables
International Journal of Stochastic Analysis, Volume 2004, Issue 2, Page 159-168, 2004., 2004 For independent random variables, the order of growth of the convergent series Sn is studied in this paper. More specifically, if the series Sn converges almost surely to a random variable, the tail series is a well‐defined sequence of random variables and converges to 0 almost surely.
Eunwoo Nam
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