Spatiotemporal-textual point processes for crime linkage detection

DOI10.1214/21-AOAS1538zbMath1498.62330arXiv1902.00440OpenAlexW4282553449MaRDI QIDQ2154224

Shixiang Zhu, Yao Xie

Publication date: 14 July 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.00440

zbMATH Keywords

variable selectioncrime linkage detectionHawkes processes with marks

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to social sciences (62P25) Learning and adaptive systems in artificial intelligence (68T05) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Training restricted Boltzmann machines: an introduction
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• A review of self-exciting spatio-temporal point processes and their applications
• First-order and stochastic optimization methods for machine learning
• Spatially inhomogeneous background rate estimators and uncertainty quantification for nonparametric Hawkes point process models of earthquake occurrences
• Training Products of Experts by Minimizing Contrastive Divergence
• The EM Algorithm and Extensions, 2E
• Estimation of Space–Time Branching Process Models in Seismology Using an EM–Type Algorithm
• Stochastic Declustering of Space-Time Earthquake Occurrences
• Topic time series analysis of microblogs
• An Introduction to the Theory of Point Processes
• Self-Exciting Point Process Modeling of Crime
• Spectra of some self-exciting and mutually exciting point processes
• A Statistical Approach to Crime Linkage

This page was built for publication: Spatiotemporal-textual point processes for crime linkage detection