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:: Volume 10, Issue 1 (8-2016) ::
JSS 2016, 10(1): 159-173 Back to browse issues page
Levy type inequality and another view of the strong law of large numbers for dependent random variables
Hamid Reza NiliSani * , Mohamma Amini , Abolghasem Bozorgnia
Abstract:   (8612 Views)

An important inequality for distribution of maximum independent random variables is Levy inequality. In this paper, a version of this inequality for weakly negative dependent random variables will be provided. The strong law for dependent random variables has been studied by different authors. In this research, also, the weighted complete convergence for arrays of rowwise negatively dependent random variables that are stochastically bounded will be obtained. complete convergence and strong law for such random variables will result.

Keywords: Levy inequality, Complete convergence, Negatively dependent random variables, Weakly negative dependent random variables
Full-Text [PDF 436 kb]   (1919 Downloads)    
Type of Study: Research | Subject: Probability & Stochastic Processes
Received: 2014/07/23 | Accepted: 2015/09/24 | Published: 2016/09/17
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NiliSani H R, Amini M, Bozorgnia A. Levy type inequality and another view of the strong law of large numbers for dependent random variables. JSS 2016; 10 (1) :159-173
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 1 (8-2016) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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