Modeling multivariate extreme events using self-exciting point processes

Spectra of some self - exciting and mutually exciting point processes

In recent years methods of data analysis for point processes have received some attention, for example, by Cox & Lewis (1966) and Lewis (1964). In particular Bartlett (1963a, b) has introduced methods of analysis based on the point spectrum. Theoretical models are relatively sparse. In this paper the theoretical properties of a class of processes with particular reference to the point spectrum ...

Self - Exciting Point Process Modeling of Crime

Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space–time clustering is modeled in se...

Self-Exciting Point Process Modeling of Crime

Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space–time clustering is modeled in se...

Modeling E-mail Networks and Inferring Leadership Using Self-Exciting Point Processes

Self-exciting point process models are used to model a social network dataset consisting of email communications between officers at West Point Academy during a one year period beginning in May 2010. The models appear to adequately capture major clustering features in the data, and features of the model may be used to predict perceived leadership status within the social network. The results su...

Self-exciting point processes with applications in finance and medicine