Uncertainty Quantification of MLE for Entity Ranking with Covariates
Abstract
We study statistical estimation and inference for the ranking problems based on pairwise comparisons with additional covariate information. In specific, in this paper, we study a Covariate-Assisted Ranking Estimation (CARE) model in a systematic way, that extends the well-known Bradley-Terry-Luce (BTL) model by incorporating the covariate information. We impose natural identifiability conditions, derive the statistical rates for the MLE under a sparse comparison graph, and obtain its asymptotic distribution. Moreover, we validate our theoretical results through large-scale numerical studies.
Cite
Text
Fan et al. "Uncertainty Quantification of MLE for Entity Ranking with Covariates." Journal of Machine Learning Research, 2024.Markdown
[Fan et al. "Uncertainty Quantification of MLE for Entity Ranking with Covariates." Journal of Machine Learning Research, 2024.](https://mlanthology.org/jmlr/2024/fan2024jmlr-uncertainty/)BibTeX
@article{fan2024jmlr-uncertainty,
title = {{Uncertainty Quantification of MLE for Entity Ranking with Covariates}},
author = {Fan, Jianqing and Hou, Jikai and Yu, Mengxin},
journal = {Journal of Machine Learning Research},
year = {2024},
pages = {1-83},
volume = {25},
url = {https://mlanthology.org/jmlr/2024/fan2024jmlr-uncertainty/}
}