Functional models for time-varying random objects

Functional data analysis provides a popular toolbox of functional models for the analysis of samples of random functions that are real valued. In recent years, samples of time-varying object data such as time-varying networks that are not in a vector space have been increasingly collected. These data can be viewed as elements of a general metric space that lacks local or global linear structure and therefore common approaches that have been used with great success for the analysis of functional data, such as functional principal component analysis, cannot be applied. We propose metric covariance, a novel association measure for paired object data lying in a metric space (omega,d) that we use to define a metric autocovariance function for a sample of random omega-valued curves, where omega generally will not have a vector space or manifold structure. The proposed metric autocovariance function is non-negative definite when the squared semimetric d(2) is of negative type. Then the eigenfunctions of the linear operator with the autocovariance function as kernel can be used as building blocks for an object functional principal component analysis for omega-valued functional data, including time-varying probability distributions, covariance matrices and time dynamic networks. Analogues of functional principal components for time-varying objects are obtained by applying Frechet means and projections of distance functions of the random object trajectories in the directions of the eigenfunctions, leading to real-valued Frechet scores. Using the notion of generalized Frechet integrals, we construct object functional principal components that lie in the metric space omega. We establish asymptotic consistency of the sample-based estimators for the corresponding population targets under mild metric entropy conditions on omega and continuity of the omega-valued random curves. These concepts are illustrated with samples of time-varying probability distributions for human mortality, time-varying covariance matrices derived from trading patterns and time-varying networks that arise from New York taxi trips.