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Activity Number:
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125
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #320100
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Title:
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Fast Covariance Estimation for Sparse Functional Data
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Author(s):
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Cai Li* and Luo Xiao and William Checkley and Ciprian Crainiceanu
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Companies:
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North Carolina State University and North Carolina State University and The Johns Hopkins University and The Johns Hopkins University
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Keywords:
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bivariate smoothing ;
FACE ;
functional data analysis ;
fPCA ;
penalized splines
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Abstract:
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We propose a novel covariance smoothing method and associated software based on penalized spline smoothing. The proposed method is a bivariate smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross validation. Our simulations demonstrate that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led by one of coauthors and to a public dataset of longitudinal CD4 counts.
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Authors who are presenting talks have a * after their name.
