scholarly journals Sufficient and necessary conditions of convergence for ρ͠ mixing random variables

2019 ◽  
Vol 17 (1) ◽  
pp. 452-464
Author(s):  
Shui-Li Zhang ◽  
Yu Miao ◽  
Cong Qu
Keyword(s):  
Complete Convergence ◽  
Necessary Conditions ◽  
Random Variables ◽  
Complete Moment Convergence ◽  
Moment Convergence ◽  
Sufficient And Necessary Conditions ◽  
Mixing Random Variables

Abstract In the present paper, the sufficient and necessary conditions of the complete convergence and complete moment convergence for ρ͠-mixing random variables are established, which extend some well-known results.

scholarly journals Complete moment convergence for Sung’s type weighted sums of ρ*-mixing random variables

Filomat ◽  
2018 ◽  
Vol 32 (4) ◽  
pp. 1447-1453 ◽  
Author(s):  
Wei Li ◽  
Pingyan Chen ◽  
Soo Sung
Keyword(s):  
Complete Convergence ◽  
Random Variables ◽  
Convergence Result ◽  
Weighted Sums ◽  
Complete Moment Convergence ◽  
Moment Convergence ◽  
Mixing Random Variables

In this paper, the authors study a complete moment convergence result for Sung?s type weighted sums of ?*-mixing random variables. This result extends and improves the corresponding theorem of Sung [S.H. Sung, Complete convergence for weighted sums of ?*-mixing random variables, Discrete Dyn. Nat. Soc. 2010 (2010), Article ID 630608, 13 pages].

scholarly journals Complete convergence and complete moment convergence for negatively dependent random variables under sub-linear expectations

Filomat ◽  
2020 ◽  
Vol 34 (4) ◽  
pp. 1093-1104
Author(s):  
Qunying Wu ◽  
Yuanying Jiang
Keyword(s):  
Probability Space ◽  
Complete Convergence ◽  
Random Variables ◽  
Complete Moment Convergence ◽  
Convergence Theorems ◽  
Dependent Random Variables ◽  
Moment Convergence ◽  
Negatively Dependent Random Variables

This paper we study and establish the complete convergence and complete moment convergence theorems under a sub-linear expectation space. As applications, the complete convergence and complete moment convergence for negatively dependent random variables with CV (exp (ln? |X|)) < ?, ? > 1 have been generalized to the sub-linear expectation space context. We extend some complete convergence and complete moment convergence theorems for the traditional probability space to the sub-linear expectation space. Our results generalize corresponding results obtained by Gut and Stadtm?ller (2011), Qiu and Chen (2014) and Wu and Jiang (2016). There is no report on the complete moment convergence under sub-linear expectation, and we provide the method to study this subject.

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