DMS Statistics and Data Science Seminar

Speaker: Dr. Jacopo Di Iorio (Emory University) 

Title: Identifying functional motifs with funBIalign

Abstract: Functional data analysis is dealing with a novel challenge: the identification of functional motifs, or “shapes” that may be repeated multiple times within each functional observation or across multiple curves belonging to the same set. To address this issue, we introduce the funBIalign algorithm, a multi-step approach employing agglomerative hierarchical clustering with complete linkage and functional distances based on mean squared residue scores and virtual error, two widely used validation measures in the biclustering literature. These distances enable funBIalign to detect functional motifs that are shifted or scaled along the y-axis. To validate the effectiveness of our methodology, we present simulations and case studies that demonstrate its ability to identify functional motifs.