Functional data analysis on nonlinear manifolds has drawn recent interest. We propose an intrinsic principal component analysis for smooth Riemannian manifold-valued functional data and study its asymptotic properties. The proposed Riemannian functional principal component analysis (RFPCA) is carried out by first mapping the manifold-valued data through Riemannian logarithm maps to tangent spaces around the time-varying Fr\'echet mean function, and then performing a classical multivariate functional principal component analysis on the linear tangent spaces. Representations of the Riemannian manifold-valued functions and the eigenfunctions on the original manifold are then obtained by mapping back with exponential maps. The tangent-space approximation through functional principal component analysis is shown to be well-behaved in terms of controlling the residual variation if the Riemannian manifold has nonnegative curvature. We derive uniform convergence rates for the model components, 查看全文>>