59 citations · 202 across the 12 of their papers we have counts for
13 papers
Optimal normal approximation for symmetric statistics
Bing-Yi Jing, Yiming Liu, Shaochen Wang +1
Rates of convergence in normal approximation are fundamental to probability and statistics. The theory has evolved from normalized sums to Studentized statistics, smooth functions…
On Convergence Rates of Spiked Eigenvalue Estimates: A General Study of Global and Local Laws in Sample Covariance Matrices
Bing-Yi Jing, Weiming Li, Jiahui Xie +2
This paper investigates global and local laws for sample covariance matrices with general growth rates of dimensions. The sample size and population dimension can have the…
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Wenjia Wang, Bing-Yi Jing
In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regress…
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…
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…
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…
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…
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…
Regularized maximum correntropy machine
Jim Jing-Yan Wang, Yunji Wang, Bing-Yi Jing +1
In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transiti…
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…
Modeling high-frequency financial data by pure jump processes
Bing-Yi Jing, Xin-Bing Kong, Zhi Liu
It is generally accepted that the asset price processes contain jumps. In fact, pure jump models have been widely used to model asset prices and/or stochastic volatilities. The que…
On normal approximations to -statistics
Vidmantas Bentkus, Bing-Yi Jing, Wang Zhou
Let be i.i.d. random observations. Let be a -statistic of order where is a linear statistic having asympt…
Saddlepoint approximation for Student's t-statistic with no moment conditions
Bing-Yi Jing, Qi-Man Shao, Wang Zhou
A saddlepoint approximation of the Student's t-statistic was derived by Daniels and Young [Biometrika 78 (1991) 169-179] under the very stringent exponential moment condition that…