Date of Award


Degree Name

Doctor of Philosophy



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.


Expectation-maximization algorithm, heterogeneous conditional dependencies, mixture of Ising graphical models


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