TY - JOUR
T1 - Uncertainty Quantification of MLE for Entity Ranking with Covariates
AU - Fan, Jianqing
AU - Hou, Jikai
AU - Yu, Mengxin
N1 - Publisher Copyright:
©2024 Jianqing Fan, Jikai Hou and Mengxin Yu.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Entity ranking
KW - High-Dimensional Inference
KW - Maximum likelihood estimator
KW - Ranking with covariates
KW - Uncertainty quantification
UR - https://www.scopus.com/pages/publications/105018577712
M3 - Article
AN - SCOPUS:105018577712
SN - 1532-4435
VL - 25
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
ER -