Multiway Component Analysis with Functional and Structural Constraints

• Activity Number: 203 - Emerging Statistical Methods for Big Tensor Data in Chemometrics and Related Fields
• Type: Invited
• Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Nathaniel Helwig*
• Companies: University of Minnesota
• Keywords: Multiway analysis ; Tensor analysis ; Component analysis ; Functional data ; Structural model

Abstract

Multiway component analysis models provide a powerful framework for discovering latent trends in longitudinal data. However, classic implementations of multiway models do not take into consideration functional information (i.e., the temporal sequence of the collected data) or structural information (i.e., which variables load onto which latent factors) about the study design. In this talk, we reveal how functional and structural constraints can be imposed in multiway models in order to elucidate trends in longitudinal data. As a motivating example, we consider a longitudinal study on per capita alcohol consumption trends conducted from 1970-2013 by the U.S. National Institute on Alcohol Abuse and Alcoholism.