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Title: Semiparametric estimation under shape invariance for fMRI data Authors:  Nicole Lazar - University of Georgia (United States) [presenting]
Cheolwoo Park - University of Georgia (United States)
Christopher Helms - University of Georgia (United States)
Abstract: The aim is to introduce a semiparametric functional data analysis approach under shape invariance for group comparisons in functional magnetic resonance imaging (fMRI) data. The components of this analysis suite include: function estimation using local polynomial regression; a shape invariant model for the relevant function estimates; evolutionary algorithms. The approach will be demonstrated on a study of practice effects.