Asynchronous and error-prone longitudinal data analysis via functional calibration

DOI10.1111/BIOM.13866zbMATH Open1543.62584MaRDI QIDQ6589281

Yehua Li, Xinyue Chang, Yi Li

Publication date: 19 August 2024

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

kernel smoothingmeasurement errorfunctional principal component analysisregression calibrationvarying coefficient modelsparse functional data

Mathematics Subject Classification ID

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

Cites Work

• Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data
• Functional data analysis.
• Survival Analysis with Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach
• Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
• Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation
• Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
• Efficient Estimation and Inferences for Varying-Coefficient Models
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains
• Functional Varying Coefficient Models for Longitudinal Data
• Regression Analysis of Sparse Asynchronous Longitudinal Data
• Selecting the Number of Principal Components in Functional Data
• Measurement Error in Nonlinear Models
• Functional Data Analysis for Sparse Longitudinal Data
• Modeling sparse longitudinal data on Riemannian manifolds

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