Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

ABSTRACT: Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data

SUBMITTER: Yang J 

PROVIDER: S-EPMC5634932 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature