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Activity Number: 125
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #320100
Title: Fast Covariance Estimation for Sparse Functional Data
Author(s): Cai Li* and Luo Xiao and William Checkley and Ciprian Crainiceanu
Companies: North Carolina State University and North Carolina State University and The Johns Hopkins University and The Johns Hopkins University
Keywords: bivariate smoothing ; FACE ; functional data analysis ; fPCA ; penalized splines

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.

Authors who are presenting talks have a * after their name.

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