On the estimation of the number of components in multivariate functional principal component analysis
Steven Golovkine, Edward Gunning, Andrew J. Simpkin, Norma Bargary
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/fast-ulxnuig/variance_mfpcaOfficialIn papernone★ 0
Abstract
Happ and Greven (2018) developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate functional feature. In this paper, we present extensive simulations to investigate choosing the number of principal components to retain. We show empirically that the conventional approach of using a percentage of variance explained threshold for each univariate functional feature may be unreliable when aiming to explain an overall percentage of variance in the multivariate functional data, and thus we advise practitioners to exercise caution.