Mohler, GeorgeFang, ShiaofenLiu, XueyingWang, HonglangHasan, Mohammad A.2022-09-152022-09-152022-08https://hdl.handle.net/1805/30001http://dx.doi.org/10.7912/C2/3024Indiana 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.en-USAttribution-NonCommercial 4.0 InternationalTemporal event sequenceHawkes processCounting ProcessSocial harmCox proportional hazard modelHeterogeneous dataThesis