Corrected confidence bands for functional data using principal components.

ABSTRACT: Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribu

SUBMITTER: Goldsmith J 

PROVIDER: S-EPMC3962763 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature