Paper

Fast Covariance Estimation for Sparse Functional Data

Luo Xiao, Cai Li, William Checkley, Ciprian Crainiceanu

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.

Publication

Statistics and Computing

Topic

Methodology

Last Updated

September, 2017

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