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Temporal Event Modeling of Social Harm with High Dimensional and Latent Covariates

Indiana University-Purdue University Indianapolis (IUPUI) / 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.

Identiferoai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/30001
Date08 1900
CreatorsLiu, Xueying
ContributorsMohler, George, Fang, Shiaofen, Wang, Honglang, Hasan, Mohammad A.
Source SetsIndiana University-Purdue University Indianapolis
Languageen_US
Detected LanguageEnglish
TypeThesis
RightsAttribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/

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