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

Description

<p>    </p>
<p>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. </p>

Links & Downloads

1. 10.25394/pgs.20339076.v1

Tags

Data mining and knowledge discovery

Graph, social and multimedia data

Information extraction and fusion

Deep learning

Semi- and unsupervised learning

Hawke Process

Latent Covariates

Social Harms

Spatial-Temporal Data

Cox Proportional Hazard Model

Temporal Event Sequence

Counting Process

Additional Fields

Identifer	oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/20339076
Date	09 September 2022
Creators	Xueying Liu (13118850)
Source Sets	Purdue University
Detected Language	English
Type	Text, Thesis
Rights	CC BY 4.0
Relation	https://figshare.com/articles/thesis/TEMPORAL_EVENT_MODELING_OF_SOCIAL_HARM_WITH_HIGH_DIMENSIONAL_AND_LATENT_COVARIATES/20339076