Bayesian scalar-on-tensor regression using the Tucker decomposition for sparse spatial modeling finds promising results analyzing neuroimaging data
| dc.creator | Spencer, Daniel | |
| dc.creator | Guhaniyogi, Rajarshi | |
| dc.creator | Prado, Raquel | |
| dc.creator | Shinohara, Russell | |
| dc.date.accessioned | 2024-09-25T16:22:04Z | |
| dc.date.available | 2024-09-25T16:22:04Z | |
| dc.date.issued | 2024-09-25 | |
| dc.description.abstract | Modeling with multidimensional arrays, or tensors, often presents a problem due to high dimensionality. In addition, these structures typically exhibit inherent sparsity, requiring the use of regularization methods to properly characterize an association between a tensor covariate and a scalar response. We propose a Bayesian method to efficiently model a scalar response with a tensor covariate using the Tucker tensor decomposition in order to retain the spatial relationship within a tensor coefficient, while reducing the number of parameters varying within the model and applying regularization methods. Simulated data are analyzed to compare the model to recently proposed methods. A neuroimaging analysis using data from the Alzheimer's Data Neuroimaging Initiative shows improved inferential performance compared with other tensor regression methods. | |
| dc.identifier.uri | http://hdl.handle.net/1969.1/1581893 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.title | Bayesian scalar-on-tensor regression using the Tucker decomposition for sparse spatial modeling finds promising results analyzing neuroimaging data | |
| dc.type | Article | |
| local.department | Statistics |