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Paper accepted to Biometrics

Structured Functional Principal Component Analysis


Haochang Shou, Vadim Zipunnikov, Ciprian M. Crainiceanu, Sonja Greven


Motivated by modern observational studies, we introduce a class of functional models that expand nested and
crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in
studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship
with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is
developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily
activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.

Link to Paper