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带动态协变量的统计排序AI 解读

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…

动态网络的双重异质性模型AI 解读

Analysis of networks that evolve dynamically requires the joint modelling of individual snapshots and time dynamics. This paper proposes a new flexible two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity to form ties with other nodes statically and the other to differentiate the tendency to retain existing ties over time. With n observed networks each having p…

超高维协变量下纵向响应的稳健模型平均预测AI 解读

Abstract Model averaging is an attractive ensemble technique to construct fast and accurate prediction. Despite of having been widely practiced in cross-sectional data analysis, its application to longitudinal data is rather limited so far. We consider model averaging for longitudinal response when the number of covariates is ultrahigh. To this end, we propose a novel two-stage procedure in which variable screening is first conducted and then followed by model averaging. In…

含协变量的有向网络模型中的统计推断AI 解读

Ting Yana http://orcid.org/0000-0001-9830-6646, Binyan Jiangb, Stephen E. Fienbergc* & Chenlei Lengd*a Department of Statistics, Central China Normal University, Wuhan, Chinab Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, Chinac Department of Statistics, Heinz College, Machine Learning Department, Cylab, Carnegie Mellon University, Pittsburgh, PAd Department of Statistics, University of Warwick and Alan Turing Institute, Coventry, UKCONTACT…