5 citations · 12 across the 9 of their papers we have counts for
10 papers
Bradley-Terry model under general comparison graphs
Ruijian Han, Yuanhang Luo, Yiming Xu
The Bradley-Terry model is a parametric model for ranking from pairwise comparisons. Existing asymptotic theory for the maximum likelihood estimator (MLE) in regimes where the numb…
Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model
Ruijian Han, Ding Lu, Yiming Xu
Zermelo's algorithm is a classical method for computing the maximum likelihood estimator in the Bradley--Terry (BT) model, but its convergence can be slow in practice. To accelerat…
Deep Ranking with Heterogeneous Effects
Yuanhang Luo, Shuxing Fang, Ruijian Han +1
Classical latent-score ranking models often fail to distinguish objects' intrinsic scores from contextual effects, which are typically nonlinear and can dominate the observed outco…
Recent advances in the Bradley--Terry Model: theory, algorithms, and applications
Shuxing Fang, Ruijian Han, Yuanhang Luo +1
This article surveys recent advances in the Bradley-Terry (BT) model and its extensions. We focus on statistical and computational aspects, with particular emphasis on the regime i…
Statistical ranking with dynamic covariates
Pinjun Dong, Ruijian Han, Binyan Jiang +1
We introduce a general covariate-assisted statistical ranking model within the Plackett--Luce framework. Unlike previous studies focusing on individual effects with fixed covariate…
Statistical inference for pairwise comparison models
Ruijian Han, Wenlu Tang, Yiming Xu
Pairwise comparison models have been widely used for utility evaluation and rank aggregation across various fields. The increasing scale of modern problems underscores the need to…
A unified analysis of likelihood-based estimators in the Plackett--Luce model
Ruijian Han, Yiming Xu
The Plackett--Luce model has been extensively used for rank aggregation in social choice theory. A central statistical question in this model concerns estimating the utility vector…
Probabilistic methods for approximate archetypal analysis
Ruijian Han, Braxton Osting, Dong Wang +1
Archetypal analysis is an unsupervised learning method for exploratory data analysis. One major challenge that limits the applicability of archetypal analysis in practice is the in…
Consistency of archetypal analysis
Braxton Osting, Dong Wang, Yiming Xu +1
Archetypal analysis is an unsupervised learning method that uses a convex polytope to summarize multivariate data. For fixed , the method finds a convex polytope with vertic…
A General Pairwise Comparison Model for Extremely Sparse Networks
Ruijian Han, Yiming Xu, Kani Chen
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 m…