Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/69908

Modeling Event Clustering Using the m-Memory Cox-Type Self-Exciting Intensity Model

Authors Chen, Feng
Chen, Kani View this author's profile
Issue Date 2014
Source International Journal of Statistics and Probability , volume 3, issue3, 126-137.
Summary In the analysis of point processes or recurrent events, the self-exciting component can be an important factor in understanding and predicting event occurrence. A Cox-type self-exciting intensity point process is generally not a proper model because of its explosion in finite time. However, the model with $m$-memory is appropriate to analyze sequences of recurrent events. It assumes the most recent $m$ events multiplicatively affect the conditional intensity of event occurrence. Aside from the interpretability, one advantage is the simplicity of the estimation and inference--the Cox partial likelihood can be applied and the resulting estimator is consistent and asymptotically normal. Another advantage is that the model can be applied to the analysis of case-cohort data via the pseudo-likelihood approach. The simulation studies support the asymptotic theory. Application is illustrated with analysis of a bladder cancer dataset and of an Australian stock index dataset, which shows evidence of self-excitation.
ISSN 1927-7032
1927-7040
Language English
Format Article
Access View full-text via DOI
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