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1	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