Document Detail

Robust, Adaptive Functional Regression in Functional Mixed Model Framework.
MedLine Citation:
PMID:  22308015     Owner:  NLM     Status:  Publisher    
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets.
Hongxiao Zhu; Philip J Brown; Jeffrey S Morris
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Publication Detail:
Journal Detail:
Title:  Journal of the American Statistical Association     Volume:  106     ISSN:  0162-1459     ISO Abbreviation:  -     Publication Date:  2011 Sep 
Date Detail:
Created Date:  2012-2-6     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  01510020R     Medline TA:  J Am Stat Assoc     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1167-1179     Citation Subset:  -    
Statistical and Applied Mathematical Sciences Institute, RTP, NC.
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Grant Support
R01 CA107304-05//NCI NIH HHS; R01 CA107304-06//NCI NIH HHS; R01 CA107304-07//NCI NIH HHS

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