Subject Area
Statistics
Abstract
This dissertation addresses the pervasive challenge of block-wise missingness in multimodal medical imaging data. To address this, we propose two novel frameworks: BaMM and IMMR. Through rigorous theoretical development, extensive simulations, and real-world applications on the ADNI dataset, these frameworks demonstrate competitive performance in prediction accuracy and uncertainty quantification. These contributions significantly advance the field of multi-modal medical data analysis by providing effective solutions for handling missing data and improving the reliability of diagnostic predictions.
Degree Date
Spring 2025
Document Type
Dissertation
Degree Name
Ph.D.
Department
Statistics and Data Science
Advisor
Jing Cao
Second Advisor
Chenyang Shen
Third Advisor
Jing Wang
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Lu, Yifan, "STATISTICAL LEARNING OF MULTIMODAL MEDICAL IMAGING DATA WITH BLOCK-WISE MISSING STRUCTURE" (2025). Statistical Science Theses and Dissertations. 49.
https://scholar.smu.edu/hum_sci_statisticalscience_etds/49
