Kernel density estimation on Riemannian manifolds

The estimation of the underlying probability density of n i.i.d. random objects on a compact Riemannian manifold without boundary is considered. The proposed methodology adapts the technique of kernel density estimation on Euclidean sample spaces to this nonEuclidean setting. Under sufficient regularity assumptions on the underlying density, L2 convergence rates are obtained.