Semiparametric estimation under shape invariance for fMRI data

Friday, April 27, 2018
Dr. Nicole Lazar, University of Georgia

SPECIAL TIME - 1:00PM Maury 104

Functional magnetic resonance imaging (fMRI) data pose many statistical challenges, owing to their size, noisiness, and complicated correlation structure. In this talk, I will give an introduction to fMRI data collection and analysis.  Then, motivated by a study of practice effects, I will introduce a semiparametric functional data analysis approach under shape invariance for group comparisons.  The components of this analysis
suite include: function estimation using local polynomial regression; a shape invariant model for the relevant function estimates; evolutionary algorithms for parameter estimation. Taken together, these steps admit a principled comparison of practice effects within and across study groups of interest.