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In the current paper we obtain discrepancy estimates in exponential Orlicz and BMO spaces in arbitrary dimension $d \ge 3$. In particular, we use dyadic harmonic analysis to prove that for the so-called digital nets of order $2$ the…

Functional Analysis · Mathematics 2015-07-10 Dmitriy Bilyk , Lev Markhasin

The irregularities of a distribution of $N$ points in the unit interval are often measured with various notions of discrepancy. The discrepancy function can be defined with respect to intervals of the form $[0,t)\subset [0,1)$ or arbitrary…

Number Theory · Mathematics 2019-11-27 Ralph Kritzinger , Markus Passenbrunner

Optimal lower bounds for discrepancy in Besov spaces with dominating mixed smoothness are known from the work of Triebel. Hinrichs proved upper bounds in the plane. In this work we systematically analyse the problem, starting with a survey…

Numerical Analysis · Mathematics 2014-02-19 Lev Markhasin

We consider the local discrepancy of a symmetrized version of Hammersley type point sets in the unit square. As a measure for the irregularity of distribution we study the norm of the local discrepancy in Besov spaces with dominating mixed…

Number Theory · Mathematics 2020-05-28 Ralph Kritzinger

Let A_N be an N-point distribution in the unit square in the Euclidean plane. We consider the Discrepancy function D_N(x) in two dimensions with respect to rectangles with lower left corner anchored at the origin and upper right corner at…

Number Theory · Mathematics 2013-10-14 Dmitriy Bilyk , Michael T. Lacey , Ioannis Parissis , Armen Vagharshakyan

We investigate the discrepancy principle for choosing smoothing parameters for kernel density estimation. The method is based on the distance between the empirical and estimated distribution functions. We prove some new positive and…

Statistics Theory · Mathematics 2015-03-19 Thoralf Mildenberger

We study here the error of numerical integration on metric measure spaces adapted to a decomposition of the space into disjoint subsets. We consider both the error for a single given function, and the worst case error for all functions in a…

Analysis of PDEs · Mathematics 2018-02-19 Luca Brandolini , William W. L. Chen , Leonardo Colzani , Giacomo Gigante , Giancarlo Travaglini

In a celebrated construction, Chen and Skriganov gave explicit examples of point sets achieving the best possible $L_2$-norm of the discrepancy function. We consider the discrepancy function of the Chen-Skriganov point sets in Besov spaces…

Numerical Analysis · Mathematics 2014-02-19 Lev Markhasin

We consider infinitely dimensional classes of functions and instead of the relative error setting, which was used in previous papers on the integral norm discretization, we consider the absolute error setting. We demonstrate how known…

Numerical Analysis · Mathematics 2022-03-15 V. N. Temlyakov

The main goal of this paper is to provide a brief survey of recent results which connect together results from different areas of research. It is well known that numerical integration of functions with mixed smoothness is closely related to…

Numerical Analysis · Mathematics 2018-12-12 Vladimir Temlyakov

The theory of digital sequences is a fundamental topic in QMC theory. Digital sequences are prototypes of sequences with low discrepancy. First examples were given by Il'ya Meerovich Sobol' and by Henri Faure with their famous…

Numerical Analysis · Mathematics 2019-04-24 Friedrich Pillichshammer

A class of distortions termed functional Bregman divergences is defined, which includes squared error and relative entropy. A functional Bregman divergence acts on functions or distributions, and generalizes the standard Bregman divergence…

Information Theory · Computer Science 2007-07-13 B. A. Frigyik , S. Srivastava , M. R. Gupta

This paper is devoted to the study of a discrepancy-type characteristic -- the fixed volume discrepancy -- of the Fibonacci point set in the unit square. It was observed recently that this new characteristic allows us to obtain optimal rate…

Numerical Analysis · Mathematics 2019-08-14 Vladimir Temlyakov , Mario Ullrich

In this paper, we propose the Fourier Discrepancy Function, a new discrepancy to compare discrete probability measures. We show that this discrepancy takes into account the geometry of the underlying space. We prove that the Fourier…

Machine Learning · Statistics 2021-11-19 Auricchio Gennaro , Codegoni Andrea , Gualandi Stefano , Zambon Lorenzo

Recalling recent results on the characterization of threshold-based sampling as quasi-isometric mapping, mathematical implications on the metric and topological structure of the space of event sequences are derived. In this context, the…

Metric Geometry · Mathematics 2018-06-19 Bernhard A. Moser

Uniform distribution of the points has been of interest to researchers for a long time and has applications in different areas of Mathematics and Computer Science. One of the well-known measures to evaluate the uniformity of a given…

Computational Geometry · Computer Science 2020-10-21 Milad Bakhshizadeh , Ali Kamalinejad , Mina Latifi

It is a well-known conjecture in the theory of irregularities of distribution that the L1 norm of the discrepancy function of an N-point set satisfies the same asymptotic lower bounds as its L^2 norm. In dimension d=2 this fact has been…

Number Theory · Mathematics 2015-09-02 Gagik Amirkhanyan , Dmitriy Bilyk , Michael T Lacey

The ostrowski inequality expresses bounds on the deviation of a function from its integral mean. The aim of this paper is to establish a new inequality using weight function which generalizes the inequalities of Dragomir, Wang and Cerone…

Classical Analysis and ODEs · Mathematics 2014-01-20 Ather Qayyum , Silvestru Sever Dragomir , Muhammad Shoaib , Muhammad Amir Latif

The spherical cap discrepancy is a prominent measure of uniformity for sets on the d-dimensional sphere. It is particularly important for estimating the integration error for certain classes of functions on the sphere. Building on a…

Combinatorics · Mathematics 2025-04-09 Holger Heitsch , René Henrion

A loss function measures the discrepancy between the true values and their estimated fits, for a given instance of data. In classification problems, a loss function is said to be proper if a minimizer of the expected loss is the true…

Information Theory · Computer Science 2020-01-03 Amichai Painsky , Gregory W. Wornell
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