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The test for normality suggested by Epps and Pulley (1983) is a serious competitor to tests based on the empirical distribution function. In contrast to the latter procedures, it has been generalized to obtain a genuine affine invariant and…

Statistics Theory · Mathematics 2021-06-29 Bruno Ebner , Norbert Henze

We propose a new powerful family of tests of univariate normality. These tests are based on an initial value problem in the space of characteristic functions originating from the fixed point property of the normal distribution in the zero…

Statistics Theory · Mathematics 2020-02-28 Bruno Ebner

Non-parametric Mann-Kendall tests for autocorrelated data rely on the assumption that the distribution of the normalized Mann-Kendall tau is Gaussian. While this assumption holds asymptotically for stationary autoregressive processes of…

Methodology · Statistics 2025-08-15 Tristan Gamot , Nils Thibeau--Sutre , Tom J. M. Van Dooren

This paper investigates the distribution of marks obtained by students across multiple courses to explore whether the data conforms to a skew-normal distribution. Traditional methods for assessing normality, such as the Shapiro Wilk test,…

Methodology · Statistics 2025-01-28 Himadri Mukherjee , Pratham Bhonge

Based on $m$-fold integrated empirical measures, we study three new classes of goodness-of-fits tests, generalizing Anderson-Darling, Cram\'er-von Mises, and Watson statistics, respectively, and examine the corresponding limiting stochastic…

Statistics Theory · Mathematics 2024-04-10 Hsien-Kuei Hwang , Satoshi Kuriki

We extend the method of rescaled Ward identities of Ameur-Kang-Makarov to study the distribution of eigenvalues close to a bulk singularity, i.e. a point in the interior of the droplet where the density of the classical equilibrium measure…

Mathematical Physics · Physics 2016-08-31 Yacin Ameur , Seong-Mi Seo

The particle number $N$ can be used as a quantitative gauge of non-Gaussianity. This idea extends to systems that are not literally finite by assigning them a notional $N$ that captures the same deviation. For an ideal gas with $N$…

Statistical Mechanics · Physics 2026-01-30 Jae Wan Shim

Transforming a random variable to improve its normality leads to a followup test for whether the transformed variable follows a normal distribution. Previous work has shown that the Anderson Darling test for normality suffers from…

Methodology · Statistics 2024-09-23 Douglas M Hawkins

We consider tests of hypotheses when the parameters are not identifiable under the null in semiparametric models, where regularity conditions for profile likelihood theory fail. Exponential average tests based on integrated profile…

Statistics Theory · Mathematics 2009-08-25 Rui Song , Michael R. Kosorok , Jason P. Fine

The status of experimental tests of general relativity and of theoretical frameworks for analyzing them are reviewed and updated. Einstein's equivalence principle (EEP) is well supported by experiments such as the Eotvos experiment, tests…

General Relativity and Quantum Cosmology · Physics 2014-08-06 Clifford M. Will

In this work, we revisit the one- and two-sample testing problems: binary hypothesis testing in which one or both distributions are unknown. For the one-sample test, we provide a more streamlined proof of the asymptotic optimality of…

Information Theory · Computer Science 2026-04-21 Arick Grootveld , Biao Chen , Venkata Gandikota

We consider a stationary linear AR($p$) model with observations subject to gross errors (outliers). The autoregression parameters are unknown as well as the distribution and moments of innoovations. The distribution of outliers $\Pi$ is…

Statistics Theory · Mathematics 2020-03-19 Michael Boldin

We propose a projection-based class of uniformity tests on the hypersphere using statistics that integrate, along all possible directions, the weighted quadratic discrepancy between the empirical cumulative distribution function of the…

In this paper, we treat the problem of testing for normality as a binary classification problem and construct a feedforward neural network that can successfully detect normal distributions by inspecting small samples from them. The…

Machine Learning · Statistics 2020-10-08 Miloš Simić

This paper is concerned with testing normality in a Hilbert space based on the maximum mean discrepancy. Specifically, we discuss the behavior of the test from two standpoints: asymptotics and practical aspects. Asymptotic normality of the…

Statistics Theory · Mathematics 2019-02-12 Natsumi Makigusa , Kanta Naito

We use a system of first-order partial differential equations that characterize the moment generating function of the $d$-variate standard normal distribution to construct a class of affine invariant tests for normality in any dimension. We…

Statistics Theory · Mathematics 2019-01-15 Norbert Henze , Jaco Visagie

The status of experimental tests of general relativity and of theoretical frameworks for analysing them is reviewed. Einstein's equivalence principle (EEP) is well supported by experiments such as the Eotvos experiment, tests of special…

General Relativity and Quantum Cosmology · Physics 2015-06-25 Clifford M. Will

Given samples from two non-negative random variables, we propose a family of tests for the null hypothesis that one random variable stochastically dominates the other at the second order. Test statistics are obtained as functionals of the…

Statistics Theory · Mathematics 2023-10-16 Tommaso Lando , Sirio Legramanti

We prove the existence of a limit distribution for the normalized normality measure $\mathcal{N}(E_N)/\sqrt{N}$ (as $N \to \infty$) for random binary sequences $E_N$, by this means confirming a conjecture of Alon, Kohayakawa, Mauduit,…

Combinatorics · Mathematics 2017-05-17 Christoph Aistleitner

This paper studies the asymptotic power of tests of sphericity against perturbations in a single unknown direction as both the dimensionality of the data and the number of observations go to infinity. We establish the convergence, under the…

Statistics Theory · Mathematics 2013-06-21 Alexei Onatski , Marcelo J. Moreira , Marc Hallin
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