Robust Bayesian General Linear Models

W D Penny, J Kilner, F Blankenburg

Research output: Contribution to journalArticle

23 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)661-671
Number of pages11
JournalNeuroImage
Volume36
Issue number3
Early online date7 May 2007
DOIs
Publication statusPublished - 1 Jul 2007

Keywords

  • Algorithms
  • Auditory Cortex
  • Bayes Theorem
  • Brain
  • Electroencephalography
  • Humans
  • Linear Models
  • Magnetic Resonance Imaging
  • Neurological Models
  • Oxygen
  • ROC Curve
  • Regression Analysis

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