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A One-Sample Test for Normality with Kernel Methods

A One-Sample Test for Normality with Kernel Methods

10 July 2015
Jérémie Kellner
Alain Celisse
ArXiv (abs)PDFHTML

Papers citing "A One-Sample Test for Normality with Kernel Methods"

7 / 7 papers shown
Title
Specification procedures for multivariate stable-Paretian laws for
  independent and for conditionally heteroskedastic data
Specification procedures for multivariate stable-Paretian laws for independent and for conditionally heteroskedastic data
S. Meintanis
John P. Nolan
C. Pretorius
124
3
0
20 Oct 2023
Composite Goodness-of-fit Tests with Kernels
Composite Goodness-of-fit Tests with Kernels
Oscar Key
Arthur Gretton
F. Briol
T. Fernandez
122
16
0
19 Nov 2021
Dimension-agnostic inference using cross U-statistics
Dimension-agnostic inference using cross U-statistics
Ilmun Kim
Aaditya Ramdas
107
19
0
10 Nov 2020
Testing for Normality with Neural Networks
Testing for Normality with Neural Networks
M. Simic
54
6
0
29 Sep 2020
On combining the zero bias transform and the empirical characteristic
  function to test normality
On combining the zero bias transform and the empirical characteristic function to test normality
B. Ebner
44
8
0
27 Feb 2020
Asymptotics and practical aspects of testing normality with kernel
  methods
Asymptotics and practical aspects of testing normality with kernel methods
Natsumi Makigusa
K. Naito
23
4
0
08 Feb 2019
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
Krishnakumar Balasubramanian
Tong Li
M. Yuan
67
28
0
24 Sep 2017
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