A unified analysis of likelihood-based estimators in the Plackett–Luce model

发布时间：2025-12-08 作者：浏览次数：

Speaker:	韩睿渐	DateTime:	2025年12月11日（周四）上午9:00-10:00
Brief Introduction to Speaker: Ruijian Han is an Assistant Professor in the Department of Data Science and Artificial Intelligence at The Hong Kong Polytechnic University. He received his Ph.D. in Mathematics from Hong Kong University of Science and Technology in 2020 and the bachelor's degree in Pure Mathematics and Applied Mathematics from Sichuan University in 2016. His research interests include ranking data analysis, high-dimensional statistics, statistical machine learning, and reinforcement learning. His work has been published in top-tier statistical journals such as JRSSB, AOS, JASA, and Biometrika.		Place:	国交2号楼315会议室
Abstract: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 that governs the model’s likelihood. In this paper, we investigate the asymptotic theory of utility vector estimation by maximizing different types of likelihood, such as full, marginal, and quasi-likelihood. Starting from interpreting the estimating equations of these estimators to gain some initial insights, we analyze their asymptotic behavior as the number of compared objects increases. In particular, we establish both uniform consistency and asymptotic normality of these estimators and discuss the trade-off between statistical efficiency and computational complexity. For generality, our results are proven for deterministic graph sequences under appropriate graph topology conditions. These conditions are shown to be informative when applied to common sampling scenarios, such as nonuniform random hype...

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