Fast Penalized Generalized Estimating Equations for Large Longitudinal Functional Datasets.

ABSTRACT: Longitudinal binary or count functional data are common in neuroscience, but are often too large to analyze with existing functional regression methods. We propose one-step penalized generalized estimating equations that supports continuous, count, or binary functional outcomes and is fast even when datasets have a large number of clusters and large cluster sizes. The method applies to both functional and scalar covariates, and the one-step estimation framework enables efficient smoothing parameter selection, bootstrapping, and joint confidence interval construction. Importantly, this semi-parametric approach yields coefficient confidence intervals that are provably valid asymptotically even under working correlation misspecification. By developing a general theory for adaptive one-step M-

SUBMITTER: Loewinger G 

PROVIDER: S-EPMC12306803 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature