Two-sample test based on maximum variance discrepancy

12/02/2020
by   Natsumi Makigusa, et al.
0

In this article, we introduce a novel discrepancy called the maximum variance discrepancy for the purpose of measuring the difference between two distributions in Hilbert spaces that cannot be found via the maximum mean discrepancy. We also propose a two-sample goodness of fit test based on this discrepancy. We obtain the asymptotic null distribution of this two-sample test, which provides an efficient approximation method for the null distribution of the test.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/22/2018

k-Sample problem based on generalized maximum mean discrepancy

In this paper we deal with the problem of testing for the quality of k p...
research
09/05/2023

Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test

Maximum mean discrepancy (MMD) refers to a general class of nonparametri...
research
04/28/2023

Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely u...
research
09/20/2018

Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks

We propose a framework for synthesis of geological images based on an ex...
research
12/11/2018

Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy

In this paper, we bound the error induced by using a weighted skeletoniz...
research
05/12/2014

FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test

The maximum mean discrepancy (MMD) is a recently proposed test statistic...
research
10/22/2020

Maximum Mean Discrepancy is Aware of Adversarial Attacks

The maximum mean discrepancy (MMD) test, as a representative two-sample ...

Please sign up or login with your details

Forgot password? Click here to reset