Bayesian adaptive selection of basis functions for functional data representation.

ABSTRACT: Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one

SUBMITTER: Sousa PHTO 

PROVIDER: S-EPMC10956930 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature