| 行m-NSD随机变量阵列的完全收敛性 |
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| 引用本文： | 冯凤香,王定成,吴群英.行m-NSD随机变量阵列的完全收敛性[J].数学杂志,2017,37(5):889-897. |
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| 作者姓名： | 冯凤香 王定成 吴群英 |
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| 作者单位： | 电子科技大学数学科学学院, 四川成都 611731;桂林理工大学理学院, 广西桂林 541004,电子科技大学数学科学学院, 四川成都 611731,桂林理工大学理学院, 广西桂林 541004 |
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| 基金项目： | Supported by National Natural Science Foundation of China (71271042; 11361019); Research Project of Guangxi High Institution (YB2014150). |
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| 摘 要： | 本文研究了行m-NSD随机变量阵列的完全收敛性问题.主要利用m-NSD随机变量的Kolmogorov型指数不等式，获得了行m-NSD随机变量阵列的完全收敛性定理，将Hu等（1998）andSung等（2005）的结果从独立情形推广到了m-NSD随机变量阵列.本文的结论同样推广了Chen等（2008），Hu等（2009），Qiu等（2011）和Wang等（2014）的结果.
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| 关 键 词： | Kolmogorov型指数不等式 完全收敛性 m-NSD随机变量 |
| 收稿时间： | 2015/8/11 0:00:00 |
| 修稿时间： | 2016/4/8 0:00:00 |
COMPLETE CONVERGENCE FOR ARRAYS OF ROWWISE M-NSD RANDOM VARIABLES |
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| FENG Feng-xiang,WANG Ding-cheng and WU Qun-ying.COMPLETE CONVERGENCE FOR ARRAYS OF ROWWISE M-NSD RANDOM VARIABLES[J].Journal of Mathematics,2017,37(5):889-897. |
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| Authors: | FENG Feng-xiang WANG Ding-cheng and WU Qun-ying |
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| Institution: | School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China;College of Science, Guilin University of Technology, Guilin 541004, China,School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China and College of Science, Guilin University of Technology, Guilin 541004, China |
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| Abstract: | In this article, we study complete convergence theorems for arrays of rowwise m-negatively superadditive-dependent (m-NSD) random variables. By using Kolmogorov-type exponential inequality for m-NSD random variables, we obtain complete convergence theorems for arrays of rowwise m-NSD random variables, which generalize those on complete convergence theorem previously obtained by Hu et al. (1998) and Sung et al. (2005) from independent distributed case to m-NSD arrays. Our results also extend the corresponding results of Chen et al.(2008), Hu et al. (2009), Qiu et al. (2011) and Wang et al. (2014). |
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| Keywords: | Kolmogorov-type exponential inequality complete convergence m-NSD random variables |
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