Online Program Home
My Program

Abstract Details

Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Biometrics Section
Abstract #330846
Title: Probabilistic Partial Least Squares Regression Applied to Longitudinal and Cross-Sectional Compositional Data
Author(s): Peter A Tait* and Paul McNicholas
Companies: McMaster Univeristy and McMaster University
Keywords: Multivariate; Matrix Variate; Tensor

Motivated by our work in pediatric research, a new partial least squares regression (PLSR) model is proposed to analyze the relationship between multiple collinear fitness and health outcomes and a series of collinear predictors, including compositional data related to daily physical activity. We develop a probabilistic version of PLSR that uses heavy tailed distributions to specify the likelihood function. The model parameters are estimated using an EM algorithm. This PLSR model is extended to tensor variate data, using the Tucker product and a variation of the multilinear tensor regression model. This extension allows us to model our collaborators' data longitudinally. All our models are implemented in Julia, a dynamic and high performance numerical computing language.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program