Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data

Lan Zhou, Jianhua Z. Huang, Josue G. Martinez, Arnab Maity, Veerabhadran Baladandayuthapani, Raymond J. Carroll

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Hierarchical functional data are widely seen in complex studies where sub-units are nested within units, which in turn are nested within treatment groups. We propose a general framework of functional mixed effects model for such data: within unit and within sub-unit variations are modeled through two separate sets of principal components; the sub-unit level functions are allowed to be correlated. Penalized splines are used to model both the mean functions and the principal components functions, where roughness penalties are used to regularize the spline fit. An EM algorithm is developed to fit the model, while the specific covariance structure of the model is utilized for computational efficiency to avoid storage and inversion of large matrices. Our dimension reduction with principal components provides an effective solution to the difficult tasks of modeling the covariance kernel of a random function and modeling the correlation between functions. The proposed methodology is illustrated using simulations and an empirical data set from a colon carcinogenesis study. Supplemental materials are available online.
Original languageEnglish (US)
Pages (from-to)390-400
Number of pages11
JournalJournal of the American Statistical Association
Volume105
Issue number489
DOIs
StatePublished - Mar 2010
Externally publishedYes

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