A0223
Title: A general pairwise comparison model for extremely sparse networks
Authors: Ruijian Han - The Hong Kong Polytechnic University (China) [presenting]
Abstract: Statistical estimation using pairwise comparison data is an effective approach to analyzing large-scale sparse networks. A general framework is proposed to model the mutual interactions in a network which enjoys ample flexibility in terms of model parameterization. Under this setup, the study shows 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 sparsity. This analysis uses a novel chaining technique and illustrates an important connection between graph topology and model consistency. The 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 the theoretical findings.
