This article considers the estimation and inference of the low-rank components in high-
dimensional matrix-variate factor models, where each dimension of the matrix-variates (p× …

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 …

In network analysis, how to estimate the number of communities K is a fundamental problem.
We consider a broad setting where we allow severe degree heterogeneity and a wide range …

A two-mode network contains two types of nodes, and edges exist only between any two
nodes that are associated with different entities. Owing to the network connections (ie …

We propose a first-order autoregressive (ie AR (1)) model for dynamic network processes in
which edges change over time while nodes remain unchanged. The model depicts the …

Abstract We propose a Degree-Corrected Block Model with Dependent Multivariate Poisson
edges (DCBM-DMP) to study stock co-jump dependence. To estimate the community …

Efforts across diverse domains like economics, energy, and agronomy have focused on
developing predictive models for time series data. A spectrum of techniques, spanning from …

In economics and social science, network data are regularly observed, and a thorough
understanding of the network community structure facilitates the comprehension of …

With the development of the internet, network data with replications can be collected at
different time points. The spatial autoregressive panel (SARP) model is a useful tool for …

While the Vector Autoregression (VAR) model has received extensive attention for modelling
complex time series, quantile VAR analysis remains relatively underexplored for high …