47: Scalar on Shape Regression Using Functional Data

Music City Center 

Functional regression is a branch of functional data analysis (FDA) that deals with using functional variables in regression models as predictors, responses,
or both. Specifically, in Scalar-on-Function (ScoF) models, some functions play the role of predictors, and some scalars are treated as responses. ScoF models have widespread applications across scientific domains and are natural extensions of the standard multivariate regression models to functional data. Our focus in this paper is on the shapes (also termed amplitudes) of functions rather than the full functions themselves. This focus is motivated, for example, by problems in neuroimaging where morphologies of anatomical parts are used to predict clinical measurements, such as disease progression or treatment effects. Accordingly, we develop a regression model, called Scalar-on-Shape (ScoSh), where the shapes of functions are treated as predictors for scalar clinical responses.

Functional regression analysis

Shape models

COVID data analysis

Functional shapes

Shape-based FDA, 

Biopharmaceutical Section