Date of Award
6-1-2023
Degree Name
Doctor of Philosophy
Department
Statistics
First Advisor
Kevin H. Lee, Ph.D.
Second Advisor
Joshua D. Naranjo, Ph.D.
Third Advisor
Yingying Zhang, Ph.D.
Fourth Advisor
Jinseok Kim, Ph.D.
Keywords
Expectation-maximization algorithm, heterogeneous conditional dependencies, mixture of Ising graphical models
Abstract
The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure when observed data come from heterogeneous resources in high-dimensional setting. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent, minorization–maximization principles and EM algorithm. A BIC-type criterion will be utilized for the selection of the tuning parameter in the penalized likelihood approaches. The effectiveness of the proposed method is supported by simulation studies and a real-world example.
Access Setting
Dissertation-Open Access
Recommended Citation
Gu, Chong, "Learning Finite Mixture of Ising Graphical Models" (2023). Dissertations. 3961.
https://scholarworks.wmich.edu/dissertations/3961