A General Pairwise Comparison Model for Extremely Sparse Networks
This provides a theoretical guarantee for estimation in large-scale sparse networks, addressing a bottleneck in data-deficient scenarios, though it is incremental in extending existing methods.
The paper tackles the problem of statistical inference in extremely sparse pairwise comparison networks by proposing a general modeling framework, and shows that the maximum likelihood estimator for latent scores is uniformly consistent under a near-minimal sparsity condition, which is sharp in leading-order asymptotics.
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.