Estimation, diagnostics, and extensions of nonparametric Hawkes processes
Date: 2 December 2022Time: 4pm AEDT
Speaker: Prof Jiancang Zhuang (Institute of Statistical Mathematics, Tokyo)
*****The link to the recording of the talk will be posted here*****
Bio: Jiancang Zhuang is an Associate Professor of statistics at the Institute of Statistical Mathematics (ISM), Tokyo. He received his PhD in statistics from the Graduate University for Advanced Studies, Japan in 2003. He worked as a post-doc fellow in ISM and UCLA from 2003 to 2007. He became a faculty member in ISM in 2007 and now is the leader of the statistical seismology research group in ISM. His main research areas are point process, and probability and statistical problems in seismology. He has more than 100 publications in statistical and goephysical journals, including JASA, JRSSB, Nature Comm., J. Geophysical Res., and Geophysical Res. Letters.
Abstract:
The Hawkes self-exciting model has become one of the most popular point-process models in many research areas in the natural and social sciences because of its capacity for investigating the clustering effect and positive interactions among individual events/particles. This article discusses a general nonparametric framework for the estimation, extensions, and post-estimation diagnostics of Hawkes models, which can be divided into 4 steps:
The Hawkes self-exciting model has become one of the most popular point-process models in many research areas in the natural and social sciences because of its capacity for investigating the clustering effect and positive interactions among individual events/particles. This article discusses a general nonparametric framework for the estimation, extensions, and post-estimation diagnostics of Hawkes models, which can be divided into 4 steps:
1. Model design. Design the model according to the features of the observation data, specifically the particular mathematical form of the Hawkes model (parametric, nonparametric, or semiparametric), which depend on the available empirical knowledge of the studied process.
2. Estimation design. Design the estimation according to the types of model formation, use the MLE method or the EM algorithm to estimate parametric model, and use stochastic reconstruction to reconstruct the nonparametric components.
3. Improvement. Improve the estimation using kernel estimates or the Bayesian method.
4. Diagnosing the new model. The reconstruction method can be naturally used as a diagnostic tool to check whether it is possible to improve the model or not.Bio: Jiancang Zhuang is an Associate Professor of statistics at the Institute of Statistical Mathematics (ISM), Tokyo. He received his PhD in statistics from the Graduate University for Advanced Studies, Japan in 2003. He worked as a post-doc fellow in ISM and UCLA from 2003 to 2007. He became a faculty member in ISM in 2007 and now is the leader of the statistical seismology research group in ISM. His main research areas are point process, and probability and statistical problems in seismology. He has more than 100 publications in statistical and goephysical journals, including JASA, JRSSB, Nature Comm., J. Geophysical Res., and Geophysical Res. Letters.
This talk will be held in hybrid mode:
In-person: RC-4082 (Level 4 Red Centre) UNSW Sydney
Online:
Link: https://unsw.zoom.us/j/82299381917?pwd=ZDRLeVZveFdDSHlOSGkxYWRMK0JXZz09Password: 017349