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Related papers: An overview of uniformity tests on the hypersphere

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We investigate orbit spaces of isometric actions on unit spheres and find a universal upper bound for the infimum of their curvatures.

Differential Geometry · Mathematics 2016-02-15 Claudio Gorodski , Alexander Lytchak

We prove Runge-type theorems and universality results for locally univalent holomorphic and meromorphic functions. Refining a result of M. Heins, we also show that there is a universal bounded locally univalent function on the unit disk.…

Complex Variables · Mathematics 2018-04-05 Daniel Pohl , Oliver Roth

Global and local regularities of functions are analyzed in anisotropic function spaces, under a common framework, that of hyperbolic wavelet bases. Local and directional regularity features are characterized by means of global quantities…

Functional Analysis · Mathematics 2012-10-09 Patrice Abry , Marianne Clausel , Stéphane Jaffard , Stéphane Roux , Béatrice Vedel

We study the probability distribution function of the long-time values of observables being time-evolved by Hamiltonians modeling clean and disordered one-dimensional chains of many spin-1/2 particles. In particular, we analyze the return…

Disordered Systems and Neural Networks · Physics 2023-10-10 I. Vallejo-Fabila , E. Jonathan Torres-Herrera

Nuclear supersymmetry is reviewed and some of its applications and extensions are discussed, together with a proposal for new, more stringent and precise tests to probe the supersymmetry classification, in particular, correlations between…

Nuclear Theory · Physics 2009-11-10 J. Barea , R. Bijker , A. Frank

We consider diffraction at random point scatterers on general discrete point sets in $\R^\nu$, restricted to a finite volume. We allow for random amplitudes and random dislocations of the scatterers. We investigate the speed of convergence…

Mathematical Physics · Physics 2007-05-23 C. Kuelske

We study the multifractal analysis of self-similar measures arising from random homogeneous iterated function systems. Under the assumption of the uniform strong separation condition, we see that this analysis parallels that of the…

Dynamical Systems · Mathematics 2019-12-23 Kathryn E. Hare , Kevin G. Hare , Sascha Troscheit

Spherical and hyperspherical data are commonly encountered in diverse applied research domains, underscoring the vital task of assessing independence within such data structures. In this context, we investigate the properties of test…

Methodology · Statistics 2024-01-23 Marija Cuparić , Bruno Ebner , Bojana Milošević

The connection between several hyperbolic type metrics is studied in subdomains of the Euclidean space. In particular, a new metric is introduced and compared to the distance ratio metric.

Metric Geometry · Mathematics 2018-01-29 Oleksiy Dovgoshey , Parisa Hariri , Matti Vuorinen

We present a brief survey of fluctuations and large deviations of particle systems with subextensive growth of the variance. These are called hyperuniform (or superhomogeneous) systems. We then discuss the relation between hyperuniformity…

Probability · Mathematics 2016-12-07 Subhro Ghosh , Joel L. Lebowitz

Distinguishing between uniform and non-uniform sample distributions is a common problem in directional data analysis; however for many tests, non-uniform distributions exist that fail uniformity rejection. By merging directional statistics…

Applications · Statistics 2011-08-11 Martin Ehler , Jennifer Galanis

We propose a new setting for testing properties of distributions while receiving samples from several distributions, but few samples per distribution. Given samples from $s$ distributions, $p_1, p_2, \ldots, p_s$, we design testers for the…

Data Structures and Algorithms · Computer Science 2019-11-19 Maryam Aliakbarpour , Sandeep Silwal

We study the universal properties of distributions of eigenvalues of random matrices in the large $N$ limit. The distributions fall in universality classes characterized entirely by the support of the spectral density.

Condensed Matter · Physics 2009-10-28 J. Ambjorn , G. Akemann

For the last two decades, high-dimensional data and methods have proliferated throughout the literature. Yet, the classical technique of linear regression has not lost its usefulness in applications. In fact, many high-dimensional…

Statistics Theory · Mathematics 2021-05-18 Arun Kumar Kuchibhotla , Lawrence D. Brown , Andreas Buja , Edward I. George , Linda Zhao

There is a growing interest in developing covariance functions for processes on the surface of a sphere due to wide availability of data on the globe. Utilizing the one-to-one mapping between the Euclidean distance and the great circle…

Applications · Statistics 2015-04-09 Jaehong Jeong , Mikyoung Jun

In statistical analysis, quantifying uncertainties through measures such as entropy, extropy, varentropy, and varextropy is of fundamental importance for understanding distribution functions. This paper investigates several properties of…

Statistics Theory · Mathematics 2025-01-28 Santosh Kumar Chaudhary , Nitin Gupta

Modeling deformations of a real object is an important task in computer vision, biomedical engineering and biomechanics. In this paper, we focus on a situation where a three-dimensional object is rotationally deformed about a fixed axis,…

Statistics Theory · Mathematics 2016-06-14 Sungkyu Jung

Hyperuniform states of matter are correlated systems that are characterized by an anomalous suppression of long-wavelength (i.e., large-length-scale) density fluctuations compared to those found in garden-variety disordered systems, such as…

Statistical Mechanics · Physics 2018-08-01 Salvatore Torquato

Universal outlier hypothesis testing is studied in a sequential setting. Multiple observation sequences are collected, a small subset of which are outliers. A sequence is considered an outlier if the observations in that sequence are…

Statistics Theory · Mathematics 2014-11-27 Yun Li , Sirin Nitinawarat , Venugopal V. Veeravalli

Deciding on the unimodality of a dataset is an important problem in data analysis and statistical modeling. It allows to obtain knowledge about the structure of the dataset, ie. whether data points have been generated by a probability…

Machine Learning · Computer Science 2021-09-10 Paraskevi Chasani , Aristidis Likas
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