CFE 2019: Start Registration
View Submission - CMStatistics
B1627
Title: The adaptive incorporation of multiple sources of information in brain imaging via penalized optimization Authors:  Damian Brzyski - Wroclaw University of Science and Technology (Poland) [presenting]
Abstract: The use of multiple sources of information in regression modeling has recently received a lot of attention in the statistical and brain imaging literature. A novel, fully-automatic statistical procedure is introduced which addresses the problem of linear regression coefficients estimation in the situation when the additional information about connectivities between variables is given. Our method, Adaptive Information Merging Estimator for Regression (AIMER) enables for the incorporation of multiple sources of such information as well as for the division of one source into pieces and determining their impact on the estimates. We performed extensive simulations to visualize the desired adjusting properties of our method and show its advantages over the existing approaches. We also applied AIMER to analyze structural brain imaging data and to reveal the association between cortical thickness and HIV-related outcomes.