报告题目：General Pairwise Comparison Models

报告时间：2024-07-01   16:30-18:00

报  告 人：韩睿渐  副教授（香港理工大学）

报告地点：理学院东北楼二楼报告厅（209）

Abstract: Statistical estimation using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this talk, we propose a general framework to model the mutual interactions in a network, which enjoys ample flexibility in terms of model parameterization. Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity. This condition is sharp in terms of the leading order asymptoticsdescribing the sparsity. Our analysis uses a novel chaining technique and illustrates an important connection between graph topology and model consistency. Our results guarantee that the maximum likelihood estimator is justified for estimation in large-scale pairwise comparison networks where data are asymptotically deficient. Simulation studies are provided in support of our theoretical findings.