activity
20052020
most citedTesting for pure-jump processes for high-frequency data

52 citations · 91 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SI20203 cited

Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition

Bing-Yi Jing, Ting Li, Zhongyuan Lyu +1

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture mul…

cs.SI2019

Measuring the Clustering Strength of a Network via the Normalized Clustering Coefficient

Ting Li, Xianshi Yu, Bing-Yi Jing

In this paper, we propose a novel statistic of networks, the normalized clustering coefficient, which is a modified version of the clustering coefficient that is robust to network…

stat.ML2019

Semi-supervised learning in unbalanced and heterogeneous networks

Ting Li, Ningchen Ying, Xianshi Yu +1

Community detection was a hot topic on network analysis, where the main aim is to perform unsupervised learning or clustering in networks. Recently, semi-supervised learning has re…

stat.ML20172 cited

Adaptive Scaling

Ting Li, Bingyi Jing, Ningchen Ying +1

Preprocessing data is an important step before any data analysis. In this paper, we focus on one particular aspect, namely scaling or normalization. We analyze various scaling meth…

math.ST201552 cited

Testing for pure-jump processes for high-frequency data

Xin-Bing Kong, Zhi Liu, Bing-Yi Jing

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistic…

math.ST201234 cited

Nonparametric estimate of spectral density functions of sample covariance matrices: A first step

Bing-Yi Jing, Guangming Pan, Qi-Man Shao +1

The density function of the limiting spectral distribution of general sample covariance matrices is usually unknown. We propose to use kernel estimators which are proved to be cons…