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.
Original language | English (US) |
---|---|
Pages (from-to) | 511-522 |
Number of pages | 12 |
Journal | Statistics and Computing |
Volume | 28 |
Issue number | 3 |
DOIs | |
State | Published - May 1 2018 |
Keywords
- Bivariate smoothing
- FACEs
- fPCA
ASJC Scopus subject areas
- Theoretical Computer Science
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics