Statistical ranking with dynamic covariates

发布时间：2025-12-08 作者：浏览次数：

Speaker:	蒋滨雁	DateTime:	2025年12月11日（周四）下午16:00-17:00
Brief Introduction to Speaker: 蒋滨雁博士于2007 年获中国科学技术大学统计学学士学位，2012 年获新加坡国立大学统计与应用概率学博士学位。博士毕业后在美国卡内基梅隆大学从事博士后工作。2015 年8 月份加入香港理工大学，现任数据科学与人工只能系担任副教授兼副系主任，并担任Hacettepe Journal of Mathematics and Statistics以及IEEE Transactions on Emerging Topics in Computational Intelligence AE。其主要研究领域是统计学，研究兴趣包括高维数据分析和网络数据分析。统计理论和建模方面的代表性成果曾在JRSSB, JASA, Biometrika, AoS, JMLR等统计学和机器学习的期刊发表。		Place:	国交二号楼315会议室
Abstract:We introduce a general covariate-assisted statistical ranking model within the Plackett–Luce framework. Unlike previous studies that focus on individual effects with fixed covariates, our model allows covariates to vary across comparisons. This added flexibility enhances model fitting but also brings significant challenges in analysis. This article addresses these challenges in the context of maximum likelihood estimation (MLE). We first provide necessary and sufficient conditions for both model identifiability and the unique existence of the MLE. Then, we develop an efficient alternating maximization algorithm to compute the MLE. Under suitable assumptions on the design of comparison graphs and covariates, we establish a uniform consistency result for the MLE, with convergence rates determined by the asymptotic connectivity of the graph sequence. We also construct random designs under which the proposed assumptions hold almost surely. Numerical studies are conducted to support our...

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