A General Pairwise Comparison Model for Extremely Sparse Networks

• URL: http://arxiv.org/abs/2002.08853v3
• Date: Thu, 10 Mar 2022 15:59:40 GMT
• Title: A General Pairwise Comparison Model for Extremely Sparse Networks
• Authors: Ruijian Han, Yiming Xu and Kani Chen
• Abstract summary: 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. Our results guarantee that the maximum likelihood estimator is justified for estimation in large-scale pairwise comparison networks.
• Score: 5.298287413134346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Statistical inference using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. In this paper, we propose a general framework to model the mutual interactions in a network, which enjoys ample flexibility in terms of model parametrization. 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 utilizes 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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