Dimension reduction methods for quantifying local variable importance and the statistical analysis of network data

Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Loyal, Joshua Daniel

Contributors dc:contributor

• Chen, Yuguo
• Zhu, Ruoqing
• Liang, Feng
• Simpson, Douglas G

• Dynamic Network
• Dynamic Multilayer Network
• Latent Space Model
• Longitudinal Network
• Mixture Model
• Network Data
• Nonparametric Bayes
• Random Forests
• Social Network
• Spike-and-Slab Prior
• Statistical Network Analysis
• Stiefel Manifold
• Sufficient Dimension Reduction
• Variable Importance
• Variational Inference

Statement dc:rights

• Copyright 2022 Joshua Loyal

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/116044

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/116044