Robust, Adaptive Functional Regression in Functional Mixed Model Framework.

ABSTRACT: 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 predictor

SUBMITTER: Zhu H 

PROVIDER: S-EPMC3270884 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature