Principal Component Analysis for Zero-Inflated Probability Measures via Lorenz Curves

Abstract

This paper develops a principal component analysis framework for probability measures associated with non-negative real-valued random variables with structural zeros. Specifically, leveraging the associated Lorenz Curve (LC) and a suitable perspective transformation of the plane, a bijective embedding is defined to treat such measures as functional data in a Hilbert space. Once the random LC is embedded in this space, standard Functional Principal Component Analysis (FPCA) becomes directly applicable, and the diffeomorphic nature of the embedding allows the main convergence results and their interpretations to be transferred back to the original space of LC. The a.s. and $L^2(\mathbb{P})$ consistency of the estimated eigenfunctions, eigenvalues, and modes of variation is subsequently investigated, both in the embedded space and, by virtue of the embedding's diffeomorphism property, in the original LC space. Crucially, this consistency analysis accounts for the double stochasticity of the estimation problem: one level of randomness is tied to the underlying LC process itself, while the other stems from the empirical estimation of the LC from sample data. As a consequence, consistency of the Karhunen--Loève reconstructions is also established, guaranteeing faithful approximation of individual curves in both spaces. Finally, the proposed FPCA methodology is illustrated on a real-world dataset of global export flows and compared with a naive FPCA approach.