@article {789,
	title = {Structured functional principal component analysis.},
	journal = {Biometrics},
	year = {2014},
	month = {2014 Oct 18},
	abstract = {<p>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.</p>},
	issn = {1541-0420},
	doi = {10.1111/biom.12236},
	author = {Shou, Haochang and Zipunnikov, Vadim and Crainiceanu, Ciprian M and Greven, Sonja}
}
@article {814,
	title = {Longitudinal scalar-on-functions regression with application to tractography data.},
	journal = {Biostatistics},
	volume = {14},
	year = {2013},
	month = {2013 Jul},
	pages = {447-61},
	abstract = {<p>We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study{\textquoteright}s primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.</p>},
	keywords = {Anisotropy, Biostatistics, Brain, Diffusion Tensor Imaging, Humans, Linear Models, Models, Statistical, Multiple Sclerosis, Principal Component Analysis, Regression Analysis},
	issn = {1468-4357},
	doi = {10.1093/biostatistics/kxs051},
	author = {Gertheiss, Jan and Goldsmith, Jeff and Crainiceanu, Ciprian and Greven, Sonja}
}
