Hierarchical functional data with mixed continuous and binary measurements

DOI10.1111/biom.12211zbMath1393.62076OpenAlexW1805566606WikidataQ30843496 ScholiaQ30843496MaRDI QIDQ3465356

Haocheng Li, John Staudenmayer, Raymond J. Carroll

Publication date: 21 January 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12211

zbMATH Keywords

accelerometrylongitudinal dataprincipal componentsmixed-effects modelpenalized splinesbinary longitudinal dataphysical activitysedentary behavior

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (8)

Ordinal probit functional outcome regression with application to computer-use behavior in rhesus monkeys ⋮ Generalized functional additive mixed models ⋮ Boosting functional response models for location, scale and shape with an application to bacterial competition ⋮ A joint design for functional data with application to scheduling ultrasound scans ⋮ Parametric Functional Principal Component Analysis ⋮ A Riemann manifold model framework for longitudinal changes in physical activity patterns ⋮ Modeling and prediction of multiple correlated functional outcomes ⋮ From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas

Uses Software

• SemiPar

Cites Work

• Functional linear regression analysis for longitudinal data
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Models for discrete longitudinal data.
• Joint modelling of paired sparse functional data using principal components
• The Analysis of Transformed Data
• Modelling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes
• Semiparametric Regression
• A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses
• Approximate Inference in Generalized Linear Mixed Models
• Improved Approximations for Multilevel Models with Binary Responses
• Functional Data Analysis for Sparse Longitudinal Data

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