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

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posted on 2022-09-09, 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