| Bayesian tensor response regression with an application to brain activation studies R Guhaniyogi, D Spencer Bayesian Analysis 16 (4), 1221-1249, 2021 | 30 | 2021 |
| Joint Bayesian estimation of voxel activation and inter-regional connectivity in fMRI experiments D Spencer, R Guhaniyogi, R Prado psychometrika 85 (4), 845-869, 2020 | 15 | 2020 |
| Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups D Spencer, YR Yue, D Bolin, S Ryan, AF Mejia NeuroImage 249, 118908, 2022 | 13 | 2022 |
| Bayesian mixed effect sparse tensor response regression model with joint estimation of activation and connectivity D Spencer, R Guhaniyogi, R Prado arXiv preprint arXiv:1904.00148, 2019 | 7 | 2019 |
| Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation F Parlak, DD Pham, DA Spencer, RC Welsh, AF Mejia Frontiers in Neuroscience 16, 1051424, 2023 | 3 | 2023 |
| Parsimonious Bayesian sparse tensor regression using the Tucker decomposition D Spencer, R Guhaniyogi, R Prado arXiv preprint arXiv:2203.04733, 2022 | 3 | 2022 |
| Inference and uncertainty quantification for high-dimensional tensor regression with tensor decompositions and Bayesian methods D Spencer University of California, Santa Cruz, 2020 | 2 | 2020 |
| Accurate estimation of functional brain connectivity via Bayesian ICA with population-derived priors A Mejia, D Bolin, D Spencer, A Eloyan arXiv preprint arXiv:2311.03791, 2023 | | 2023 |
| Fast Bayesian estimation of brain activation with cortical surface fMRI data using EM DA Spencer, D Bolin, AF Mejia arXiv preprint arXiv:2211.01429, 2022 | | 2022 |
| Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling D Spencer, R Guhaniyogi, R Shinohara, R Prado arXiv preprint arXiv:2203.04733, 2022 | | 2022 |
| Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM D Spencer, D Bolin, MB Nebel, A Mejia arXiv preprint arXiv:2203.00053, 2022 | | 2022 |
| danieladamspencer/BayesGLM_Validation: This is a repository to keep track of code that is used to produce the analyses and figures for the BayesGLM Validation paper D Spencer, YR Yue, D Bolin, S Ryan, AF Mejia Github, 2021 | | 2021 |
Amanda MejiaAssociate Professor, Department of Statistics, Indiana UniversityVerifierad e-postadress på iu.edu
