Functional principal component analysis for longitudinal data with informative dropout
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Publication:6627921
DOI10.1002/sim.8798zbMATH Open1546.62682WikidataQ101577740 ScholiaQ101577740MaRDI QIDQ6627921
Haolun Shi, Jiguo Cao, Liang-Liang Wang, Jianghu Dong
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
functional data analysisfiltration ratesinformative missingkidney glomerular likelihoodorthonormal empirical basis functions
Cites Work
- Functional linear regression analysis for longitudinal data
- Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
- Methodology and convergence rates for functional linear regression
- Supervised functional principal component analysis
- Weak convergence for the covariance operators of a Hilbertian linear process.
- Tensor product splines and functional principal components
- Partially functional linear regression in high dimensions
- Interpretable functional principal component analysis
- A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds
- Improving ultimate convergence of an augmented Lagrangian method
- Functional Data Analysis for Sparse Longitudinal Data
- Parametric Functional Principal Component Analysis
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