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TEMPORAL EVENT MODELING OF SOCIAL HARM WITH HIGH DIMENSIONAL AND LATENT COVARIATES

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thesis
posted on 09.09.2022, 14:00 authored by Xueying LiuXueying Liu

    

The counting process is the fundamental of many real-world problems with event data. Poisson process, used as the background intensity of Hawkes process, is the most commonly used point process. The Hawkes process, a self-exciting point process fits to temporal event data, spatial-temporal event data, and event data with covariates. We study the Hawkes process that fits to heterogeneous drug overdose data via a novel semi-parametric approach. The counting process is also related to survival data based on the fact that they both study the occurrences of events over time. We fit a Cox model to temporal event data with a large corpus that is processed into high dimensional covariates. We study the significant features that influence the intensity of events. 

History

Degree Type

Doctor of Philosophy

Department

Computer Science

Campus location

Indianapolis

Advisor/Supervisor/Committee Chair

George Mohler

Advisor/Supervisor/Committee co-chair

Shiaofen Fang

Additional Committee Member 2

Mohammad Hasan

Additional Committee Member 3

Honglang Wang

Additional Committee Member 4

Murat Dundar