Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States

DOI10.1080/07350015.2021.2025065arXiv1807.10853OpenAlexW4206099194MaRDI QIDQ6190335

Xuening Zhu, Ganggang Xu, Hansheng Wang, Jingfei Zhang, Yongtao Guan, Biao Cai

Publication date: 5 March 2024

Published in: Journal of Business & Economic Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.10853

zbMATH Keywords

composite likelihoodsocial mediacomposite EM algorithmclustered point processesnonoverlapping clusters

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Cites Work

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• On the convergence properties of the EM algorithm
• Estimation of Space–Time Branching Process Models in Seismology Using an EM–Type Algorithm
• Log Gaussian Cox Processes
• An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes
• Residual Analysis for Spatial Point Processes (with Discussion)
• Spectra of some self-exciting and mutually exciting point processes

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