Strong Uniform Consistency with Rates for Kernel Density Estimators with General Kernels on Manifolds
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We provide a strong uniform consistency result with the convergence rate for the kernel density estimation on Riemannian manifolds with Riemann integrable kernels (in the ambient Euclidean space). We also provide a strong uniform consistency result for the kernel density estimation on Riemannian manifolds with Lebesgue integrable kernels. The kernels considered in this paper are different from the kernels in the Vapnik-Chervonenkis class that are frequently considered in statistics society. We illustrate the difference when we apply them to estimate probability density function. We also provide the necessary and sufficient condition for a kernel to be Riemann integrable on a submanifold in the Euclidean space.
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