Date of Award
2023
Degree Type
Thesis
Degree Name
Master of Science in Statistics
Department
Computer Science and Statistics
First Advisor
Jing Wu
Abstract
This project aimed to investigate the differences in terms of network structural characteristics between full and sampled networks and to develop a new weighted generalized estimating equations model (WGEE) in order to mitigate the sampling bias and obtain less biased sample estimates for model coefficients and standard errors. The applications of such an approach are of interest in the field of network analysis and public health studies. The Transmission Reduction Intervention Project (TRIP) is an HIV study using risk networks and is a key motivator for this work. Two other motivating examples were constructed where it was found that sampled networks often do not share similar values for some key network characteristics. To attempt to reduce bias induced by these sampled networks, multiple weight functions were applied to each potential edge using four different approaches. Then a weighted generalized estimating equations (WGEE) model was fit on a simulated full network and a sample from that network using a computationally efficient one-step WGEE method. The results of this simulation show that bias is present in the unweighted case when compared to what is observed in the full network. Weighting approaches show a promising direction for future work on addressing bias in WGEE models in networks.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Humphreys, Ryan, "RECOVERING THE FULL NETWORK USING A SAMPLING BIAS ADJUSTMENT IN SAMPLED NETWORK" (2023). Open Access Master's Theses. Paper 2348.
https://digitalcommons.uri.edu/theses/2348