Fast Covariance Estimation for Sparse Functional Data

2016/03/18 by Luo Xiao, Li Cai, Xiao, Luo +5 · 3 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Epidemiology

paper · pdf · doi:10.48550/arxiv.1603.05758

openalex publication_date 2016/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Penalized Functional Regression

Cited by

• Bayesian semiparametric modelling of biomarker variability in joint models
• Bayesian Multivariate Sparse Functional Principal Components Analysis
• Localized Functional Principal Component Analysis Based on Covariance Structure

Related

• Fast Covariance Estimation for Multivariate Sparse Functional Data
• Fast symmetric additive covariance smoothing
• Efficient Covariance Estimation for Sparsified Functional Data
• A novel smoothing-based goodness-of-fit test of covariance for multivariate sparse functional data
• Matrix-free Penalized Spline Smoothing with Multiple Covariates