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                            Library & Multimedia
 A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications
NCJ Number
              253381
Journal

*Statistical Science* Volume: 33 Issue: 3 Dated: 2018 Pages: 299-318
Author(s)
          Alex Reinhart
Date Published

2018
Length
              20 pages
Annotation
              This review of self-exciting spatio-temporal point processes and their applications explains the basic theory, surveys related estimation and inference techniques from each field, highlights several key applications, and suggests directions for future research.
Abstract
              Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in fields where spatio-temporal clustering of events is observed, such as earthquake modeling, infectious disease, and crime. In the past several decades, advances have been made in estimation, inference, simulation, and diagnostic tools for self-exciting point process models. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2016-R2-CX-0021


  
  
        
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  Publication Type
        Issue Overview
                                Instructional Material


  
  
        
  Award
        Point process modeling with spatiotemporal covariates for predicting crime
    
  
  
Date Published: January 1, 2018
    
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