Modeling and prediction of multiple correlated functional outcomes

DOI10.1007/s13253-018-00344-0zbMath1426.62314OpenAlexW2903623499WikidataQ92910466 ScholiaQ92910466MaRDI QIDQ1722632

Jiguo Cao, David Ruppert, Kunlaya Soiaporn, Raymond J. Carroll

Publication date: 18 February 2019

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6447061

zbMATH Keywords

diffusion tensor imagingmultiple sclerosisGaussian copulasskewed functional datatractography data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Uses Software

• fda (R)
• SemiPar
• refund
• gamair
• orthogonalsplinebasis

Cites Work

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• Tables for Computing Bivariate Normal Probabilities
• Fast methods for spatially correlated multilevel functional data
• Hierarchical functional data with mixed continuous and binary measurements
• Semiparametric Regression
• Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skewt-Distribution
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• Dynamical Correlation for Multivariate Longitudinal Data