Strong Uniform Consistency with Rates for Kernel Density Estimators with General Kernels on Manifolds

07/13/2020
by   Hau-tieng Wu, et al.
1

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/07/2016

Normalizing Flows on Riemannian Manifolds

We consider the problem of density estimation on Riemannian manifolds. D...
research
03/31/2023

Pointwise density estimation on metric spaces and applications in seismology

We are studying the problem of estimating density in a wide range of met...
research
09/04/2020

Density estimation and modeling on symmetric spaces

In many applications, data and/or parameters are supported on non-Euclid...
research
05/24/2017

Consistent Kernel Density Estimation with Non-Vanishing Bandwidth

Consistency of the kernel density estimator requires that the kernel ban...
research
05/12/2018

Kernel and wavelet density estimators on manifolds and more general metric spaces

We consider the problem of estimating the density of observations taking...
research
12/23/2019

Kernel Embedding Linear Response

In the paper, we study the problem of estimating linear response statist...
research
02/07/2014

Two-stage Sampled Learning Theory on Distributions

We focus on the distribution regression problem: regressing to a real-va...

Please sign up or login with your details

Forgot password? Click here to reset