Bayesian Wavelet-packet Historical Functional Linear Models.

ABSTRACT: Historical Functional Linear Models (HFLM) quantify associations between a functional predictor and functional outcome where the predictor is an exposure variable that occurs before, or at least concurrently with, the outcome. Prior work on the HFLM has largely focused on estimation of a surface that represents a time-varying association between the functional outcome and the functional exposure. This existing work has employed frequentist and spline-based estimation methods, with little attention paid to formal inference or adjustment for multiple testing and no approaches that implement wavelet-bases. In this work, we propose a new functional regression model that estimates the time-varying, lagged association between a functional outcome and a functional exposure. Building off of recent

SUBMITTER: Meyer MJ 

PROVIDER: S-EPMC9624484 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature