Results 11 to 20 of about 1,020,025 (280)
Multilevel sparse functional principal component analysis. [PDF]
Stat, 2014 We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis was proposed recently for such data when functions are ...
Di C, Crainiceanu CM, Jank WS.
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U.S. Adolescent Rest-Activity patterns: insights from functional principal component analysis (NHANES 2011–2014) [PDF]
International Journal of Behavioral Nutrition and Physical Activity, 2023 Background Suboptimal rest-activity patterns in adolescence are associated with worse health outcomes in adulthood. Understanding sociodemographic factors associated with rest-activity rhythms may help identify subgroups who may benefit from ...
Chris Ho Ching Yeung +4 more
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Fast Multilevel Functional Principal Component Analysis. [PDF]
J Comput Graph Stat, 2023 We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA (Di et al., 2009).
Cui E, Li R, Crainiceanu CM, Xiao L.
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Local Functional Principal Component Analysis [PDF]
Complex Analysis and Operator Theory, 2007 Covariance operators of random functions are crucial tools to study the way random elements concentrate over their support. The principal component analysis of a random function X is well-known from a theoretical viewpoint and extensively used in practical situations. In this work we focus on local covariance operators.
Mas, André
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Multilevel functional principal component analysis
The Annals of Applied Statistics, 2009 Published in at http://dx.doi.org/10.1214/08-AOAS206 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Di, Chong-Zhi +3 more
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Longitudinal Viral Load Clustering for People With HIV Using Functional Principal Component Analysis [PDF]
AIDS Research and TreatmentDiscussion: The findings highlight the impact of continuous clustering in understanding the distinct viral profiles of PWH and emphasize the importance of tailored treatment and insights to target interventions for all PWH.
Yunqing Ma +5 more
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Trajectory modeling of gestational weight: A functional principal component analysis approach. [PDF]
PLoS ONE, 2017 Suboptimal gestational weight gain (GWG), which is linked to increased risk of adverse outcomes for a pregnant woman and her infant, is prevalent. In the study of a large cohort of Canadian pregnant women, our goals are to estimate the individual weight ...
Menglu Che +3 more
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Rest-activity profiles among U.S. adults in a nationally representative sample: a functional principal component analysis [PDF]
International Journal of Behavioral Nutrition and Physical Activity, 2022 Background The 24-h rest and activity behaviors (i.e., physical activity, sedentary behaviors and sleep) are fundamental human behaviors essential to health and well-being.
Qian Xiao +5 more
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Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data [PDF]
BMC Medical Research Methodology, 2022 Background Longitudinal studies are important to understand patterns of growth in children and limited in India. It is important to identify an approach for characterising growth trajectories to distinguish between children who have healthy growth and ...
Reka Karuppusami +2 more
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Structured functional principal component analysis. [PDF]
Biometrics, 2015 Summary 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
Shou H +3 more
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