Latent Network Structure Learning From High-Dimensional Multivariate Point Processes

• 成果类型: Article
• 署名作者: Cai, Biao; Zhang, Jingfei; Guan, Yongtao
• 署名单位: University of Miami
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2102019
• 发表日期: 2024
• 页码: 95-108
• 关键词: model selection; regularization; inequalities; statistics; STABILITY; DYNAMICS; Lasso

摘要

Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, we might wish to estimate the neuronal functional connectivity network based on spiking times recorded from a collection of neurons. To characterize the complex processes underlying the observed data, we propose a new and flexible class of nonstationary Hawkes processes that allow both excitatory and inhibitory effects. We estimate the latent network structure using an efficient sparse least squares estimation approach. Using a thinning representation, we establish concentration inequalities for the first and second order statistics of the proposed Hawkes process. Such theoretical results enable us to establish the non-asymptotic error bound and the selection consistency of the estimated parameters. Furthermore, we describe a least squares loss based statistic for testing if the background intensity is constant in time. We demonstrate the efficacy of our proposed method through simulation studies and an application to a neuron spike train dataset. Supplementary materials for this article are available online.