2026-09-19T04:49:08Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/492732026-02-16T02:29:54Zcom_1773_4888col_1773_4900

Lila, Eardi EL Sun, Xiaoyan 2022-09-23T20:43:22Z 2022-09-23T20:43:22Z 2022-09-23 2022 Sun_washington_0250O_24863.pdf http://hdl.handle.net/1773/49273 Thesis (Master's)--University of Washington, 2022 We introduce a penalized discriminant analysis method for multivariate functional data supported on compact 1D domains, motivated by an application that aims to identify subjects with poor cognitive status from diffusion MRI data. By leveraging a connection to the optimal scoring problem, we bypass minimizing complex objective functions directly and recast the problem into a penalized regression framework. The proposed formulation leads to an efficient model. The classifier achieved adequate prediction performance on the challenging diffusion MRI dataset. Simulation studies showed that the univariate classifier achieves satisfactory performance compared to existing methods. As an extension of the methodology proposed, we also present a multi-class classifier that can accommodate multiple outcome categories. application/pdf en_US CC BY Statistics Biostatistics Penalized discriminant analysis for multivariate functional data Thesis Open Access open.access