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arXiv · 1603.05758

Fast Covariance Estimation for Sparse Functional Data

Luo Xiao·Cai Li·William Checkley·Ciprian M. Crainiceanu
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Abstract

Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline 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 show 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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Luo Xiao, Cai Li, William Checkley, Ciprian M. Crainiceanu. 2016-03-18. Fast Covariance Estimation for Sparse Functional Data. https://arxiv.org/abs/1603.05758

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