Major Papers


Tensor data is widely used in modern data science. The interest lies in identifying and characterizing the relationship between tensor datasets and external covariates. These datasets, though, are often incomplete. An efficient nonconvex alternating updating algorithm proposed by J. Zhou et al. in the paper "Partially Observed Dynamic Tensor Response Regression" provides a novel approach. The algorithm handles the problem of unobserved entries by solving an optimization problem of a loss function under the low-rankness, sparsity, and fusion constraints. This analysis aims to understand in detail the proposed algorithms and their theoretical proofs with, potentially, dropping some of the assumptions implied to the model. Also, the efficiency and accuracy of the algorithms on a simulated data and Parkinson's disease real-life dataset will be illustrated.

Primary Advisor

Dr. Sévérien Nkurunziza

Program Reader

Dr. Abdul A. Hussein

Degree Name

Master of Science


Mathematics and Statistics

Document Type

Major Research Paper

Convocation Year