ArticleStat (International Statistical Institute)2020
Fast Covariance Estimation for Multivariate Sparse Functional Data.
Article in Stat (International Statistical Institute), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.BMC medical research methodology · 2025Pooled it
- Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.Statistical methods in medical research · 2026Article
- Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis.Biostatistics (Oxford, England) · 2025Article
- Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's Disease.Statistics in medicine · 2025Article
- Efficient Bayesian functional principal component analysis of irregularly-observed multivariate curves.Computational statistics & data analysis · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference.Journal of the American Statistical Association · 2025Article
- Projection-based two-sample inference for sparsely observed multivariate functional data.Biostatistics (Oxford, England) · 2024Article
- SEMIPARAMETRIC BIVARIATE HIERARCHICAL STATE SPACE MODEL WITH APPLICATION TO HORMONE CIRCADIAN RELATIONSHIP.The annals of applied statistics · 2024Article
- TENSOR REGRESSION FOR INCOMPLETE OBSERVATIONS WITH APPLICATION TO LONGITUDINAL STUDIES.The annals of applied statistics · 2024Article
- Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach.BMC medical research methodology · 2024Article
- Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.Statistical methods in medical research · 2024Article
- Modeling longitudinal data using matrix completion.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.Statistics and its interface · 2024Article
- Latent factor model for multivariate functional data.Biometrics · 2023Article
- Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data.BMC cancer · 2023Article
- BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.The annals of applied statistics · 2023Article
- Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.Statistics in medicine · 2023Article
- A patient-centric modeling framework captures recovery from SARS-CoV-2 infection.Nature immunology · 2023Article
- Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease.Biometrics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Covariance estimation is essential yet underdeveloped for analyzing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional data using bivariate penalized splines. The tensor-product B-spline formulation of the proposed method enables a simple spectral decomposition of the associated covariance operator and explicit expressions of the resulting eigenfunctions as linear combinations of B-spline bases, thereby dramatically facilitating subsequent principal component analysis. We derive a fast algorithm for selecting the smoothing parameters in covariance smoothing using leave-one-subject-out cross-validation. The method is evaluated with extensive numerical studies and applied to an Alzheimer's disease study with multiple longitudinal outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.