Estimation of Functional Principal Components from Sparse Functional Data

TL;DR

This paper introduces a new method for estimating functional principal components from sparse, irregularly observed data using basis expansion and maximum likelihood, improving accuracy and applicability.

Contribution

It proposes a novel approach combining basis expansion with maximum likelihood estimation and orthogonalization for sparse functional data analysis.

Findings

Method performs well in simulations

Outperforms existing approaches

Successfully applied to real datasets

Abstract

Sparse functional data arise when measurements are observed infrequently and at irregular time points for each subject, often in the presence of measurement error. These characteristics introduce additional challenges for functional principal component analysis. In this paper, we propose a new approach for extracting functional principal components from such data by combining basis expansion with maximum likelihood estimation. Orthogonality of the estimated eigenfunctions is preserved throughout the optimization using modified Gram-Schmidt orthonormalization. An information criterion is proposed to select both the optimal number of basis functions and the rank of the covariance structure. Principal component scores are subsequently estimated via conditional expectation, enabling accurate reconstruction of the underlying functional trajectories across the full domain despite sparse observations. Simulation studies demonstrate the effectiveness of the proposed method and show that it performs favorably compared with existing approaches. Its practical utility is illustrated through applications to CD4 cell count data from the Multicenter AIDS Cohort Study and somatic cell count data from Irish research dairy cattle. Supplementary materials, including technical details, additional simulation results, and the R package mGSFPCA, are available online.