5 papers
Causal Inference under Interference: Regression Adjustment and Optimality
Xinyuan Fan, Chenlei Leng, Weichi Wu
In randomized controlled trials without interference, regression adjustment is widely used to enhance the efficiency of treatment effect estimation. This paper extends this efficie…
Low-Rank Graphon Learning for Networks
Xinyuan Fan, Feiyan Ma, Chenlei Leng +1
Graphons offer a powerful framework for modeling large-scale networks, yet estimation remains challenging. We propose a novel approach that leverages a low-rank additive representa…
Random Interval Distillation for Detection of Change-Points in Markov Chain Bernoulli Networks
Xinyuan Fan, Weichi Wu
We propose a new and generic approach for detecting multiple change-points in dynamic networks with Markov formation, termed random interval distillation (RID). By collecting rando…
A Random Graph-based Autoregressive Model for Networked Time Series
Weichi Wu, Chenlei Leng
Contemporary time series data often feature objects connected by a social network that naturally induces temporal dependence involving connected neighbours. The network vector auto…
Tractably Modelling Dependence in Networks Beyond Exchangeability
Weichi Wu, Sofia Olhede, Patrick Wolfe
We propose a general framework for modelling network data that is designed to describe aspects of non-exchangeable networks. Conditional on latent (unobserved) variables, the edges…