Advancing Model Selection in Generalized Functional Regression Through Multivariate Functional PCA

Oregon Convention Center 

In the realm of generalized functional regression, interpreting results from multivariate functional principal component analysis (MFPCA) applied to diverse, multi-dimensional functional data can be complex. This study introduces an advanced model selection technique that leverages a forward selection approach in MFPCA Here, functional variables are incrementally integrated, with their inclusion in the model being determined by a user-selected criterion. This method is adaptable to sparse data or data plagued with measurement errors. We benchmark the effectiveness of this novel approach against existing methods. A key application of this methodology is demonstrated in a study of neonate metabolites, with the goal of understanding the relationship between longitudinal trajectories and a binary morbidity outcome. This research marks a significant step forward in refining model selection strategies within generalized functional regression frameworks using MFPCA.

Functional principal component analysis

Model selection

Generalized functional regression

Longitudinal data

Multivariate functional principal component analysis

Forward selection 

Section on Nonparametric Statistics