Nested Hierarchical Functional Data Modeling and Inference for the Analysis of Functional Plant Phenotypes

DOI10.1080/01621459.2017.1366907zbMath1398.62352OpenAlexW2745915427MaRDI QIDQ4962426

Yehua Li, Dan Nettleton, Yuhang Xu

Publication date: 2 November 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1242&context=stat_las_pubs

zbMATH Keywords

Akaike information criterionprincipal componentsfunctional data analysispenalized splinespermutation testgeneralized likelihood ratio test

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (5)

Predictive functional linear models with diverging number of semiparametric single-index interactions ⋮ Fast Multilevel Functional Principal Component Analysis ⋮ Latent group detection in functional partially linear regression models ⋮ Functional multiple-outcome model in application to multivariate growth curves of infant data ⋮ From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas

Uses Software

• fda (R)
• SemiPar

Cites Work

• Unnamed Item
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• Semiparametric Regression
• Semiparametric Regression for Clustered Data Using Generalized Estimating Equations
• Principal component models for sparse functional data
• Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves
• Multilevel Functional Clustering Analysis
• Selecting the Number of Principal Components in Functional Data
• Multilevel Cross‐Dependent Binary Longitudinal Data
• Penalized Spline Models for Functional Principal Component Analysis
• Functional Data Analysis for Sparse Longitudinal Data

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