Generalized Heterogeneous Functional Model for Data-Driven Subgroup Discovery
A generalized functional regression method that simultaneously estimates subject-specific functional coefficient curves and identifies latent subgroups by fusing similar coefficient functions with a pairwise fusion penalty, without pre-specifying the number of subgroups. A pre-clustering step produces a finer-than-truth partition of subjects to reduce parameters and make estimation scalable to large mobile-health datasets, demonstrated on UK Biobank wearable activity data for mental-disorder and Parkinson's disease risk.
Generalized Heterogeneous Functional Model with Applications to Large-scale Mobile Health Data
Wearable devices produce large-scale physical-activity curves, and the association between activity and disease risk plausibly differs across latent subgroups of people. This paper proposes a general…