Fast covariance estimation for sparse functional data

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...

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Bibliographic Details
Published inStatistics and computing Vol. 28; no. 3; pp. 511 - 522
Main Authors Xiao, Luo, Li, Cai, Checkley, William, Crainiceanu, Ciprian
Format Journal Article
LanguageEnglish
Published New York Springer US 01.05.2018
Springer Nature B.V
Subjects
Artificial Intelligence
Bivariate analysis
Computer simulation
Covariance
Data analysis
Data smoothing
Mathematics and Statistics
Probability and Statistics in Computer Science
Splines
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Bivariate smoothing
FACEs
fPCA
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Summary: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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ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-017-9744-8