Thesis (Ph.D.)--University of Washington, 2023Statistical machine learning techniques offer versatile tools for prediction, estimation and inference across a wide range of applications. However, the ability of existing methods to handle data with dependence induced by complex spatial or network structures is limited, despite the increasing potential of such data due to recent advances in data collection technologies. This dissertation develops statistical machine learning methodologies that are well suited for such settings and require weaker assumptions than many existing alternatives. We start our discussion with an intuitive variable importance measure for a broad class of black-box spatial prediction models in Chapter 2. We then introdu...
Andrew Wade Phase transitions for random spatial networks Spatial networks I Networks are everywhere...
We summarize and discuss the current state of spatial point process theory and directions for future...
This thesis develops new models and inference methods for network structures, and contains two parts...
Many physical quantities around us vary across space or space-time. An example of a spatial quantity...
Discrete spatial structures are ubiquitous in statistical analysis. They can take the form of images...
Thesis (Ph.D.)--University of Washington, 2020In this dissertation, we develop new principled applic...
There has been a recent increase in the use of network models for representing interactions and stru...
The focus of this dissertation is on extending targeted learning to settings with complex unknown de...
This dissertation focuses on prediction and inference problems for complex spatiotemporal systems. I...
In social sciences, data structures are often hierarchical. When these data also arise in spatial se...
Machine learning and artificial intelligence (ML/AI), previously considered black box approaches, ar...
Geo-AI is a discipline that leverages both artificial intelligence and geographical information syst...
Over the past few decades, addressing "spatial confounding" has become a major topic in spatial stat...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
Random forest and similar Machine Learning techniques are already used to generate spatial predictio...
Andrew Wade Phase transitions for random spatial networks Spatial networks I Networks are everywhere...
We summarize and discuss the current state of spatial point process theory and directions for future...
This thesis develops new models and inference methods for network structures, and contains two parts...
Many physical quantities around us vary across space or space-time. An example of a spatial quantity...
Discrete spatial structures are ubiquitous in statistical analysis. They can take the form of images...
Thesis (Ph.D.)--University of Washington, 2020In this dissertation, we develop new principled applic...
There has been a recent increase in the use of network models for representing interactions and stru...
The focus of this dissertation is on extending targeted learning to settings with complex unknown de...
This dissertation focuses on prediction and inference problems for complex spatiotemporal systems. I...
In social sciences, data structures are often hierarchical. When these data also arise in spatial se...
Machine learning and artificial intelligence (ML/AI), previously considered black box approaches, ar...
Geo-AI is a discipline that leverages both artificial intelligence and geographical information syst...
Over the past few decades, addressing "spatial confounding" has become a major topic in spatial stat...
Summarization: This book provides an inter-disciplinary introduction to the theory of random fields ...
Random forest and similar Machine Learning techniques are already used to generate spatial predictio...
Andrew Wade Phase transitions for random spatial networks Spatial networks I Networks are everywhere...
We summarize and discuss the current state of spatial point process theory and directions for future...
This thesis develops new models and inference methods for network structures, and contains two parts...