Asymptotic comparison of identifying constraints for Bradley-Terry models
Statistics Theory
2022-05-10 v1 Methodology
Statistics Theory
Abstract
The Bradley-Terry model is widely used for pairwise comparison data analysis. In this paper, we analyze the asymptotic behavior of the maximum likelihood estimator of the Bradley-Terry model in its logistic parameterization, under a general class of linear identifiability constraints. We show that the constraint requiring the Bradley-Terry scores for all compared objects to sum to zero minimizes the sum of the variances of the estimated scores, and recommend using this constraint in practice.
Cite
@article{arxiv.2205.04341,
title = {Asymptotic comparison of identifying constraints for Bradley-Terry models},
author = {Weichen Wu and Brian W. Junker and Nynke M. D. Niezink},
journal= {arXiv preprint arXiv:2205.04341},
year = {2022}
}