Abstract

Incorporating covariate information into functional data analysis methods can substantially improve modeling and prediction performance. However, many functional data analysis methods do not make use of covariate or supervision information, and those that do often have high computational cost or assume that only the scores are related to covariates, an assumption that is usually violated in practice. In this article, we propose a functional data analysis framework that relates both the mean and covariance function to covariate information. To facilitate modeling and ensure the covariance function is positive semi-definite, we represent it using splines and design a map from Euclidean space to the symmetric positive semi-definite matrix manifold. Our model is combined with a roughness penalty to encourage smoothness of the estimated functions in both the temporal and covariate domains. We also develop an efficient method for fast evaluation of the objective and gradient functions. Cross-validation is used to choose the tuning parameters. We demonstrate the advantages of our approach through a simulation study and an astronomical data analysis.

Keywords Supervised functional principal component analysis, Computational efficiency, Covariate information, Penalized likelihood, Astrostatistics