Model-Based Clustering of Nonparametric Weighted Networks
Open Access
Author:
Agarwal, Amal
Graduate Program:
Statistics
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 15, 2019
Committee Members:
Lingzhou Xue, Thesis Advisor/Co-Advisor Zhibiao Zhao, Committee Member Ephraim Mont Hanks, Program Head/Chair
Keywords:
Exponential-family random graphical model Local likelihood Variational inference
Abstract:
Water pollution is a major global environmental problem, and it poses a great environmental
risk to public health and biological diversity. This work is motivated by assessing the
potential environmental threat of coal mining through increased sulfate concentrations in
river networks, which do not belong to any simple parametric distribution. However, existing
network models mainly focus on binary or discrete networks and weighted networks
with known parametric weight distributions. We propose a principled nonparametric
weighted network model based on exponential-family random graph models and local
likelihood estimation and study its model-based clustering with application to largescale
water pollution network analysis. We do not require any parametric distribution
assumption on network weights. The proposed method greatly extends the methodology
and applicability of statistical network models. Furthermore, it is scalable to large and
complex networks in large-scale environmental studies and geoscientific research. The
power of our proposed methods is demonstrated in extensive simulation studies.
