scholarly journals On complete convergence of triangular arrays of independent random variables

2007 ◽  
Vol 77 (10) ◽  
pp. 952-963 ◽  
Author(s):  
István Berkes ◽  
Michel Weber
Keyword(s):  
Complete Convergence ◽  
Random Variables ◽  
Independent Random Variables ◽  
Triangular Arrays

scholarly journals Limit theorems for triangular arrays under a relaxed asymptotic negligibility condition

1987 ◽  
Vol 42 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Miloslav Jirina
Keyword(s):  
Asymptotic Behavior ◽  
Classical Theory ◽  
Limit Theorems ◽  
Random Variables ◽  
Triangular Array ◽  
Independent Random Variables ◽  
Triangular Arrays

AbstractLet {Xnk} be a triangular array of independent random variables satisfying the so-called tail-negligibility condition, i.e. such that Prob{|Xnk| > a} → 0 as both k, n → ∞. It is also assumed that for each fixed k, Xnk converges in distribution as n → ∞. Theorems on the asymptotic behavior of the row sums of the array, analogous to those of the classical theory under the uniform negligibility condition, are presented.

scholarly journals Complete convergence for weighted sums of pairwise independent random variables

2017 ◽  
Vol 15 (1) ◽  
pp. 467-476
Author(s):  
Li Ge ◽  
Sanyang Liu ◽  
Yu Miao
Keyword(s):  
Rate Of Convergence ◽  
Complete Convergence ◽  
Moving Average ◽  
Random Variables ◽  
Independent Random Variables ◽  
Weighted Sums ◽  
Pairwise Independent Random Variables ◽  
Moving Average Processes

Abstract In the present paper, we have established the complete convergence for weighted sums of pairwise independent random variables, from which the rate of convergence of moving average processes is deduced.

scholarly journals Complete convergence for arrays of ratios of order statistics

2019 ◽  
Vol 17 (1) ◽  
pp. 439-451
Author(s):  
Yu Miao ◽  
Huanhuan Ma ◽  
Shoufang Xu ◽  
Andre Adler
Keyword(s):  
Order Statistics ◽  
Order Statistic ◽  
Complete Convergence ◽  
Pareto Distribution ◽  
Random Variables ◽  
Independent Random Variables

Abstract Let {Xn,k, 1 ≤ k ≤ mn, n ≥ 1} be an array of independent random variables from the Pareto distribution. Let Xn(k) be the kth largest order statistic from the nth row of the array and set Rn,in,jn = Xn(jn)/Xn(in) where jn < in. The aim of this paper is to study the complete convergence of the ratios {Rn,in,jn, n ≥ 1}.

scholarly journals Complete Convergence of the Maximum Partial Sums for Arrays of Rowwise of AANA Random Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Aiting Shen ◽  
Ranchao Wu ◽  
Yan Chen ◽  
Yu Zhou
Keyword(s):  
Complete Convergence ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Independent Random Variables ◽  
Partial Sums ◽  
Positive Real ◽  
Strong Law ◽  
Limiting Behavior ◽  
Large Numbers ◽  
Rowwise Independent

The limiting behavior of the maximum partial sums(1/an)max1≤j≤n|∑i=1j‍Xni|is investigated, and some new results are obtained, where{Xni,i≥1,n≥1}is an array of rowwise AANA random variables and{an,n≥1}is a sequence of positive real numbers. As an application, the Chung-type strong law of large numbers for arrays of rowwise AANA random variables is obtained. The results extend and improve the corresponding ones of Hu and Taylor (1997) for arrays of rowwise independent random variables.

On complete convergence for weighted sums of arrays of rowwise END random variables and its statistical applications

2019 ◽  
Vol 69 (1) ◽  
pp. 223-232
Author(s):  
Xiaohan Bao ◽  
Junjie Lin ◽  
Xuejun Wang ◽  
Yi Wu
Keyword(s):  
Complete Convergence ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Independent Random Variables ◽  
Weighted Sums ◽  
Nonparametric Regression Model ◽  
Strong Law ◽  
Mild Conditions ◽  
Large Numbers ◽  
End Random Variables

Abstract In this paper, the complete convergence for the weighted sums of arrays of rowwise extended negatively dependent (END, for short) random variables is established under some mild conditions. In addition, the Marcinkiewicz-Zygmund type strong law of large numbers for arrays of rowwise END random variables is also obtained. The result obtained in the paper generalizes and improves some corresponding ones for independent random variables and some dependent random variables in some extent. By using the complete convergence that we established, we further study the complete consistency for the weighted estimator in a nonparametric regression model based on END errors.

scholarly journals Complete moment convergence for weighted sums of negatively orthant dependent random variables

Filomat ◽  
2017 ◽  
Vol 31 (5) ◽  
pp. 1195-1206 ◽  
Author(s):  
Xuejun Wang ◽  
Zhiyong Chen ◽  
Ru Xiao ◽  
Xiujuan Xie
Keyword(s):  
Complete Convergence ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Independent Random Variables ◽  
Weighted Sums ◽  
Complete Moment Convergence ◽  
Strong Law ◽  
Associated Random Variables ◽  
Moment Convergence ◽  
Large Numbers

In this paper, the complete moment convergence and the integrability of the supremum for weighted sums of negatively orthant dependent (NOD, in short) random variables are presented. As applications, the complete convergence and the Marcinkiewicz-Zygmund type strong law of large numbers for NODrandom variables are obtained. The results established in the paper generalize some corresponding ones for independent random variables and negatively associated random variables.

scholarly journals On Complete Convergence for Weighted Sums ofρ*-Mixing Random Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Aiting Shen ◽  
Xinghui Wang ◽  
Huayan Zhu
Keyword(s):  
Complete Convergence ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Independent Random Variables ◽  
Weighted Sums ◽  
Strong Law ◽  
Large Numbers ◽  
Mixing Random Variables ◽  
Independent And Identically Distributed

We prove the strong law of large numbers for weighted sums∑i=1n‍aniXi, which generalizes and improves the corresponding one for independent and identically distributed random variables andφ-mixing random variables. In addition, we present some results on complete convergence for weighted sums ofρ*-mixing random variables under some suitable conditions, which generalize the corresponding ones for independent random variables.

scholarly journals On Complete Convergence for Weighted Sums of Arrays of Dependent Random Variables

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Soo Hak Sung
Keyword(s):  
Complete Convergence ◽  
Random Variables ◽  
Independent Random Variables ◽  
Weighted Sums ◽  
Dependent Random Variables ◽  
Negatively Associated ◽  
Negatively Dependent Random Variables ◽  
Rowwise Independent ◽  
Mixing Random Variables

A rate of complete convergence for weighted sums of arrays of rowwise independent random variables was obtained by Sung and Volodin (2011). In this paper, we extend this result to negatively associated and negatively dependent random variables. Similar results for sequences ofφ-mixing andρ*-mixing random variables are also obtained. Our results improve and generalize the results of Baek et al. (2008), Kuczmaszewska (2009), and Wang et al. (2010).

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