Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies
Year: 2017
Type: article
Source: NeuroImage
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, Alzheimer's Association, Bristol-Myers Squibb, Alzheimer's Drug Discovery Foundation, Eli Lilly and Company, Pfizer, Merck, Roche, Cancer Prevention and Research Institute of Texas, Biogen, AbbVie, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Takeda Pharmaceutical Company, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, GE Healthcare, Canadian Institutes of Health Research, National Institute on Aging, National Institute of Mental Health, National Cancer Institute, National Institute of General Medical Sciences
Awards: G1006888324, G104586243, G2423387114, G2835601961, Statistical and Applied Mathematical Sciences Institute, Developing Dynamic Tools for Analyzing Irregularly Spaced Longitudinal Affect Data, G4233579262, Advanced Statistical Tools for Ultra-High Dimensional Functional Data with Spatial-Temporal Correlation, Statistical Methods for Cancer Clinical Trials, Alzheimer's Disease Neuroimaging Initiative, Statistical Analysis of Biomedical Imaging Data in Curved Space, G6881754079, Bayesian Approaches to Model Selection for Survival Data
OpenAlex ID: W2581851498