Temporal Event Modeling of Social Harm with High Dimensional and Latent Covariates
Abstract
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.
Degree
Ph.D.
Advisors
Mohler, Purdue University.
Subject Area
Public health|Toxicology|Clinical psychology|Information science|Pharmaceutical sciences|Psychology|Web Studies
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