activity
20132022
most citedAsymptotic optimality and efficient computation of the leave-subject-out cross-validation

35 citations · 39 across the 4 of their papers we have counts for

collaborators

10 papers

stat.ME2022★ 2 cited

Simultaneous Estimation and Group Identification for Network Vector Autoregressive Model with Heterogeneous Nodes

Xuening Zhu, Ganggang Xu, Jianqing Fan

Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregr…

stat.ME2022

Bias-correction and Test for Mark-point Dependence with Replicated Marked Point Processes

Ganggang Xu, Jingfei Zhang, Yehua Li +1

Mark-point dependence plays a critical role in research problems that can be fitted into the general framework of marked point processes. In this work, we focus on adjusting for ma…

stat.ML2021★ 2 cited

Row-clustering of a Point Process-valued Matrix

Lihao Yin, Ganggang Xu, Huiyan Sang +1

Structured point process data harvested from various platforms poses new challenges to the machine learning community. By imposing a matrix structure to repeatedly observed marked…

stat.ML2021

Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory

Ruiqi Liu, Ganggang Xu, Zuofeng Shang

When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification…

stat.ME2020

Second order semi-parametric inference for multivariate log Gaussian Cox processes

Kristian Bjørn Hessellund, Ganggang Xu, Yongtao Guan +1

This paper introduces a new approach to inferring the second order properties of a multivariate log Gaussian Cox process (LGCP) with a complex intensity function. We assume a semi-…

stat.ME2020

Group Network Hawkes Process

Guanhua Fang, Ganggang Xu, Haochen Xu +2

In this work, we study the event occurrences of individuals interacting in a network. To characterize the dynamic interactions among the individuals, we propose a group network Haw…