数学科学研究所
Insitute of Mathematical Science

Applied Mathematical Seminar 52: General Pairwise Comparison Models

Seminar| Institute of Mathematical Sciences

Time: FridayDecember 8th, 2023 , 16:00-17:00
LocationRS408, IMS

Speaker: Ruijian Han, The Hong Kong Polytechnic University (PolyU)


AbstractStatistical 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 asymptotics describing 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.


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