Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis

• Activity Number: 172 - Risk Prediction and Analysis
• Type: Contributed
• Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Amal Agarwal* and Lingzhou Xue
• Companies: Pennsylvania State University and Penn State University and National Institute of Statistical Sciences
• Keywords: Nonparametric Estimation; Model-Based Clustering; Local Likelihood; Exponential-Family Random Graphical Model; Variational Inference; Big Data in Environmental Studies

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 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 large-scale 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 geo-scientific research. The power of our proposed methods is demonstrated in simulation studies and a real application to sulfate pollution network analysis in Ohio watershed in Pennsylvania, USA.