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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Showing 1 to 2 of 2 (0.062 seconds)

Spelling suggestions: "subject:"emporal every sequence"" "subject:"atemporal every sequence""

1

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

Liu, Xueying 08 1900 (has links)
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.
Temporal event sequence
Hawkes process
Counting Process
Social harm
Cox proportional hazard model
Heterogeneous data
2

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

Xueying Liu (13118850) 09 September 2022 (has links)
<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>
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

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