题目：Inference in Semiparametric Formation Models for Directed Networks

报告人：晏挺 教授 （华中师范大学）

地点：致远楼108室

时间：2024年9月20日 16:00-17:30

Abstract：We propose a semiparametric model for dyadic link formations in directed networks.The model contains a set of degree parameters that measure different effects of popularity or outgoingness across nodes, a regression parameter vector that reflects the homophily effect resulting from the nodal attributes or pairwise covariates associated with edges, and a set of latent random noises with unknown distributions. Our interest lies in inferring the unknown degree parameters and homophily parameters. The dimension of the degree parameters increases with the number of nodes. Under the high-dimensional regime, we develop a kernel-based least squares approach to estimate the unknown parameters. The major advantage of our estimator is that it does not encounter the incidental parameter problem for the homophily parameters. We prove consistency of all the resulting estimators of the degree parameters and homophily parameters. We establish high-dimensional central limit theorems for the proposed estimators and provide several applications of our general theory, including testing the existence of degree heterogeneity, testing sparse signals and recovering the support. Simulation studies and a real data application are conducted to illustrate the finite sample performance of the proposed methods.

报告人简介: 晏挺，现任华中师范大学数学与统计学学院教授，中国科学技术大学博士毕业，曾在乔治华盛顿大学做博士后研究，主要从事网络数据分析和成对比较的研究工作，主持了包含优秀青年基金项目，面上项目等多项国家自然科学基金项目，在Annals of Statistics, Journal of the American Statistical Association, Biometrika，Journal of Machine Learning Research上等发表了多篇论文。

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