A note on modeling sparse exponential-family functional response curves.

Non-Gaussian functional data are considered and modeling through functional principal components analysis (FPCA) is discussed. The direct extension of popular FPCA techniques to the generalized case incorrectly uses a marginal mean estimate for a model that has an inherently conditional interpretation, and thus leads to biased estimates of population and subject-level effects. The methods proposed address this shortcoming by using either a two-stage or joint estimation strategy. The performance of all methods is compared numerically in simulations. An application to ambulatory heart rate monitoring is used to further illustrate the distinctions between approaches.