CFE 2019: Start Registration
View Submission - CMStatistics
Title: Spatially correlated functional data analysis: Application to brain imaging Authors:  Surajit Ray - University of Glasgow (United Kingdom) [presenting]
Salihah Alghamdi - University of Glasgow (United Kingdom)
Abstract: A model is proposed for analysing replicated functional data which are spatially correlated. The research stems from the need for accurate estimation of spatio-temporal fields by summarising information observed over several replicates. The framework generalizes the existing framework of spatio-temporal regression model with partial differential equations regularisation (ST-PDE) approach, and thus can accommodate spatially dependent functions or time dependent surfaces embedded in manifolds and irregular boundaries. This need has emerged from a study on classification of brain signals based on the difference in visual stimulus. Analytically, we show that the estimators of composite spatio-temporal field is relatively more efficient than existing estimators. The proposed method is thoroughly compared via simulation studies to existing spatio-temporal functional techniques and is applied to the analysis of the EEG data on brain signals to provide a composite temporally varying brain map over several replications.