Temporal Event Modeling of Social Harm with High Dimensional and Latent Covariates
dc.contributor.advisor | Mohler, George | |
dc.contributor.advisor | Fang, Shiaofen | |
dc.contributor.author | Liu, Xueying | |
dc.contributor.other | Wang, Honglang | |
dc.contributor.other | Hasan, Mohammad A. | |
dc.date.accessioned | 2022-09-15T10:56:50Z | |
dc.date.available | 2022-09-15T10:56:50Z | |
dc.date.issued | 2022-08 | |
dc.degree.date | 2022 | en_US |
dc.degree.grantor | Purdue University | en_US |
dc.degree.level | Ph.D. | en_US |
dc.description | Indiana University-Purdue University Indianapolis (IUPUI) | en_US |
dc.description.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. | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/30001 | |
dc.identifier.uri | http://dx.doi.org/10.7912/C2/3024 | |
dc.language.iso | en_US | en_US |
dc.rights | Attribution-NonCommercial 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
dc.subject | Temporal event sequence | en_US |
dc.subject | Hawkes process | en_US |
dc.subject | Counting Process | en_US |
dc.subject | Social harm | en_US |
dc.subject | Cox proportional hazard model | en_US |
dc.subject | Heterogeneous data | en_US |
dc.title | Temporal Event Modeling of Social Harm with High Dimensional and Latent Covariates | en_US |
dc.type | Thesis | en |
thesis.degree.discipline | Computer & Information Science | en |