Functional principal component analysis as an alternative to mixed-effect models for describing sparse repeated measures in presence of missing data

DOI10.1002/sim.10214MaRDI QIDQ6663856

Cécile Proust-Lima, Corentin Ségalas, Catherine Helmer, Robin Genuer

Publication date: 15 January 2025

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

missing datamixed modelsfunctional principal component analysissparse functional data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Random-Effects Models for Longitudinal Data
• From sparse to dense functional data and beyond
• Longitudinal data with follow-up truncated by death: match the analysis method to research aims
• Principal components analysis of sampled functions
• Linear mixed models in practice: a SAS-oriented approach
• Gauss-Hermite quadrature approximation for estimation in generalized linear mixed models
• A survey of functional principal component analysis
• Functional data analysis.
• Joint Models for Longitudinal and Time-to-Event Data
• Inference and missing data
• Informative Drop-Out in Longitudinal Data Analysis
• Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains
• Longitudinal and time‐to‐drop‐out joint models can lead to seriously biased estimates when the drop‐out mechanism is at random
• Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia
• Functional Data Analysis for Sparse Longitudinal Data
• Elucidating Age and Sex-Dependent Association Between Frontal EEG Asymmetry and Depression: An Application of Multiple Imputation in Functional Regression
• Linear mixed models for longitudinal data
• Functional Data Analysis for Longitudinal Data with Informative Observation Times
• Bias-Correction and Test for Mark-Point Dependence with Replicated Marked Point Processes
• Modern multiple imputation with functional data
• A hypothesis testing procedure for random changepoint mixed models
• Using simulation studies to evaluate statistical methods

This page was built for publication: Functional principal component analysis as an alternative to mixed-effect models for describing sparse repeated measures in presence of missing data

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6663856)