Abstract Number:
2307
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
James Merchant (1), Hyunkeun Cho (2)
Institutions:
(1) University of Iowa, Iowa City, IA, (2) N/A, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Functional principal component analysis|Model selection|Generalized functional regression|Longitudinal data|Multivariate functional principal component analysis| Forward selection
Sponsors:
Section on Nonparametric Statistics
Tracks:
Statistical Methods for Functional Data
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