In this dissertation we present a compilation of the research conducted during the author’s doctoral program. In the first part, we discuss a case study regarding the impact of scholar-ships on student success at Oregon State University (OSU). Specifically, we look at the grad-uation and retention rates and aim to...
In this dissertation I will demonstrate a novel application of self-exciting point process models to mass shooting data. I will also introduce two adaptations to the traditional nonparametric Hawkes process modeling framework. One such modification allows for the estimation of the additional productivity introduced by an event that is not...
Analysis of observations on sequential events over time is common in real life. Sequential measurements over time describing the behavior of systems are usually called time series data, which have been collected in a wide range of disciplines. Over the years there have been multiple research areas in studying stochastic...
Agent-based models (ABM) are widely used in network data analysis, and due to their simple structures and sophisticated outcomes, they serve as good tools in understanding the dynamics in networks. In this thesis, we develop an agent-based dynamic network model, and show that it can replicate the expected degree distribution...
In areas such as spatial analysis and time series analysis, it is essential to understand and quantify spatial or temporal heterogeneity. In this dissertation, we focus on a spatially varying coefficient model, in which spatial heterogeneity is accommodated by allowing the regression coefficients to vary in a given spatial domain....
Successive sampling population size estimation (SS-PSE) is a method used by government agencies and aid organizations around the world to estimate the size of hidden populations using data from respondent-driven sampling (RDS) surveys. SS-PSE ad-dresses a specific need in estimation and helps us evaluate the vulnerability of areas to HIV...
Objective: The goal of this project is to recreate aspects of the article “Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images” (Yoo et al., 2019), where methods for predicting LCZ classes for four large cities throughout the world...
Anomaly detection aims at detecting the points that appear different than the majority of the data, such that they are suspected to be generated from a different distribution. Anomaly detectors have been applied in many different fields, such as detecting fraudulent behaviors in bank transaction, finding broken sensors in a...
Diverse scientific fields collect multiple time series data to investigate the dynamical behavior of complex systems: atmospheric and climate science, geophysics, neuroscience, epidemiology, ecology, and environmental science. Identifying patterns of mutual dependence among such data generates valuable knowledge that can be applied either for inferential or forecasting purposes. Vector autoregressive...
The interactions between microbial taxa have been under great research interest in the sci- ence community given the microbiome data deluge. Several methods have been proposed to model and estimate the conditional dependency between microbial taxa for their interac- tions, in order to eliminate spurious correlation detections. However, these methods...