On complete convergence for widely orthant-dependent random variables and its applications in nonparametric regression models

By Xuejun Wang, Chen Xu, Tien-Chung Hu, Andrei Volodin, Shuhe Hu. 2014

173 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/97ee8168cd050f27eae21383e7eb9f774bef1544

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Aiting Shen
2. Shuhe Hu
3. Xuejun Wang
4. Andrei Volodin

In Management Science and Operations Research

1. Aiting Shen
2. Shuhe Hu
3. Xuejun Wang
4. Andrei Volodin
5. Mei Yao
6. Wenjuan Wang
7. Zhiyong Chen
8. Chen Xu

In Statistics and Probability

1. Aiting Shen
2. Shuhe Hu
3. Xuejun Wang
4. Andrei Volodin
5. Mei Yao
6. Wenjuan Wang
7. Zhiyong Chen
8. Chen Xu

Citations in the corpus

• Complete convergence for weighted sums of NSD random variables and its application in the EV regression model (2014, methodology): “For the proof, one can refer to Wu (2006), Shen (2013), Shen and Wu (2013), or Wang et al. (2014b) Lemma 2.6 Let {Xn, n ≥ 1} be an array of rowwise random variables which is stochastically dominated by a random variable X.”
• Exponential probability inequalities for WNOD random variables and their applications (2016, background): “[31] obtained the following inequality:”
• Complete convergence for weighted sums of END random variables and its application to nonparametric regression models (2016, result): “Hence, the main results of the paper generalise the corresponding one of Wang et al. (2014b).”