title: Robust Bayesian General Linear Models.
abstract: We describe a Bayesian learning algorithm for Robust General Linear Models (RGLMs). The noise is modeled as a Mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides a robust estimation of regression coefficients. A variational inference framework is used to prevent overfitting and provides a model order selection criterion for noise model order. This allows the RGLM to default to the usual GLM when robustness is not required. The method is compared to other robust regression methods and applied to synthetic data and fMRI.
authors.affiliation: Wellcome Department of Imaging Neuroscience, University College London, London WC1N 3BG, UK. w.penny@fil.ion.ucl.ac.uk
authors.forename: W D
authors.initials: WD
authors.lastname: Penny
authors.forename: J
authors.initials: J
authors.lastname: Kilner
authors.forename: F
authors.initials: F
authors.lastname: Blankenburg
mesh_terms: D000465:Algorithms; D001303:Auditory Cortex / Q000033:anatomy & histology / Q000502:physiology; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D004569:Electroencephalography; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D008959:Models, Neurological; D010100:Oxygen / Q000097:blood; D012372:ROC Curve; D012044:Regression Analysis
chemical_list: D010100:Oxygen