Latent Network Structure Learning From High-Dimensional Multivariate Point Processes

DOI10.1080/01621459.2022.2102019arXiv2004.03569OpenAlexW3015784949MaRDI QIDQ6153972

Biao Cai, Yongtao Guan, Jingfei Zhang

Publication date: 19 March 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.03569

zbMATH Keywords

nonstationaryselection consistencymultivariate Hawkes processnonlinear Hawkes processnon asymptotic error bound

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Locally stationary Hawkes processes
• An approach to modeling asymmetric multivariate spatial covariance structures
• A Bernstein type inequality and moderate deviations for weakly dependent sequences
• Variable selection in nonparametric additive models
• Additive regression and other nonparametric models
• Dynamics of pattern formation in lateral-inhibition type neural fields
• Estimating the dimension of a model
• Stability results for a general class of interacting point processes dynamics, and applications
• Adjusted regularization in latent graphical models: application to multiple-neuron spike count data
• A unified theory of confidence regions and testing for high-dimensional estimating equations
• Generalized likelihood ratio statistics and Wilks phenomenon
• Exponential inequalities for martingales, with application to maximum likelihood estimation for counting processes
• Lasso and probabilistic inequalities for multivariate point processes
• Stability of nonlinear Hawkes processes
• First- and Second-Order Statistics Characterization of Hawkes Processes and Non-Parametric Estimation
• Emergence of Scaling in Random Networks
• Extended Bayesian information criteria for model selection with large model spaces
• Estimation of Space–Time Branching Process Models in Seismology Using an EM–Type Algorithm
• Power-Law Distributions in Empirical Data
• On Lewis' simulation method for point processes
• A cluster process representation of a self-exciting process
• Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
• Sparse Additive Models
• A Multiscale Community Blockmodel for Network Exploration
• 10.1162/153244303322753670
• Decomposition of Variance for Spatial Cox Processes
• Regularization Parameter Selections via Generalized Information Criterion
• Separating Spike Count Correlation from Firing Rate Correlation
• Minimax-optimal rates for sparse additive models over kernel classes via convex programming
• Inference for a Nonstationary Self-Exciting Point Process with an Application in Ultra-High Frequency Financial Data Modeling
• Spectra of some self-exciting and mutually exciting point processes
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity
• Modelling microstructure noise with mutually exciting point processes

This page was built for publication: Latent Network Structure Learning From High-Dimensional Multivariate Point Processes