English
Related papers

Related papers: Partial reconstruction of measures from halfspace …

200 papers

A union of an arrangement of affine hyperplanes $H$ in $R^d$ is the real algebraic variety associated to the principal ideal generated by the polynomial $p_{H}$ given as the product of the degree one polynomials which define the hyperplanes…

The reconstruction of quantum states from a sufficient set of experimental data can be achieved with arbitrarily weak measurement interactions. Since such weak measurements have negligible back-action, the quantum state reconstruction is…

Quantum Physics · Physics 2010-03-16 Holger F. Hofmann

We study the problem of reconstructing and predicting the future of a dynamical system by the use of time-delay measurements of typical observables. Considering the case of too few measurements, we prove that for Lipschitz systems on…

Dynamical Systems · Mathematics 2024-01-30 Krzysztof Barański , Yonatan Gutman , Adam Śpiewak

Given positive measures $\nu,\mu$ on an arbitrary measurable space $(\Omega, \mathcal F)$, we construct a sequence of finite partitions $(\pi_n)_n$ of $(\Omega, \mathcal F)$ s.t. $$ \sum_{A\in \pi_n: \mu(A)>0} 1_{A} \frac{\nu(A)}{\mu(A)}…

Classical Analysis and ODEs · Mathematics 2019-09-10 Oleksii Mostovyi , Pietro Siorpaes

In this paper we show that if $\mu$ is a Borel measure in $\mathbb R^{n+1}$ with growth of order $n$, so that the $n$-dimensional Riesz transform $R_\mu$ is bounded in $L^2(\mu)$, and $B\subset\mathbb R^{n+1}$ is a ball with $\mu(B)\approx…

Classical Analysis and ODEs · Mathematics 2017-09-18 Daniel Girela-Sarrión , Xavier Tolsa

In this paper we develop a general framework of badly approximable points in a metric space $X$ equipped with a $\sigma$-finite doubling Borel regular measure $\mu$. We establish that under mild assumptions the $\mu$-measure of the set of…

Number Theory · Mathematics 2023-07-20 Victor Beresnevich , Shreyasi Datta , Anish Ghosh , Benjamin Ward

We consider the measure-geometric Laplacians $\Delta^{\mu}$ with respect to atomless compactly supported Borel probability measures $\mu$ as introduced by Freiberg and Z\"ahle in 2002 and show that the harmonic calculus of $\Delta^{\mu}$…

Functional Analysis · Mathematics 2017-10-10 Marc Kesseböhmer , Tony Samuel , Hendrik Weyer

Let $\mu$ be a Borel measure on $R^d$ which may be non doubling. The only condition that $\mu$ must satisfy is $\mu(B(x,r))\leq C r^n$ for all $x\in R^d$, $r>0$, and for some fixed $0<n\leq d$. In this paper, we develop Littlewood-Paley…

Classical Analysis and ODEs · Mathematics 2007-05-23 Xavier Tolsa

We show that whenever a separable subset $S$ of a complete metric space $X$ admits a $d$-dimensional weak tangent field, the set $S$ is close to being $d$-dimensional in the following sense. Whenever $\mu$ is a Borel finite measure on $X$…

Metric Geometry · Mathematics 2026-04-20 Jakub Takáč

We discuss boundedness and compactness properties of the embedding $M_\Lambda^1\subset L^1(\mu)$, where $M_\Lambda^1$ is the closure of the monomials $x^{\lambda_n}$ in $L1([0,1])$ and $\mu$ is a finite positive Borel measure on the…

Functional Analysis · Mathematics 2014-02-17 Isabelle Chalendar , Emmanuel Fricain , Dan Timotin

The computation of the Tukey depth, also called halfspace depth, is very demanding, even in low dimensional spaces, because it requires the consideration of all possible one-dimensional projections. In this paper we propose a random depth…

Computation · Statistics 2007-07-03 J. A. Cuesta-Albertos , A. Nieto-Reyes

We consider the numbers of positive and negative eigenvalues of matrices of squared distances between randomly sampled i.i.d. points in a given metric measure space. These numbers and their limits, as the number of points grows, in fact…

Metric Geometry · Mathematics 2025-08-12 Alexey Kroshnin , Tianyu Ma , Eugene Stepanov

Data depth is a concept in multivariate statistics that measures the centrality of a point in a given data cloud in $\IR^d$. If the depth of a point can be represented as the minimum of the depths with respect to all one-dimensional…

Computation · Statistics 2020-07-17 Rainer Dyckerhoff , Pavlo Mozharovskyi , Stanislav Nagy

Tukey's depth offers a powerful tool for nonparametric inference and estimation, but also encounters serious computational and methodological difficulties in modern statistical data analysis. This paper studies how to generalize and compute…

Methodology · Statistics 2023-05-04 Yiyuan She , Shao Tang , Jingze Liu

In this paper, we expand the theory of depth-unbiased source localization to unbiased parameter estimation and signal reconstruction of an arbitrary number of non-zero parameters to be recovered. The topic touches on the concept of exact…

Information Theory · Computer Science 2026-05-08 Joonas Lahtinen

We prove that every finite Borel measure $\mu$ in $\mathbb{R}^N$ that is bounded from above by the Hausdorff measure $\mathcal{H}^s$ can be split in countable many parts $\mu\lfloor_{E_k}$ that are bounded from above by the Hausdorff…

Classical Analysis and ODEs · Mathematics 2025-02-05 Antoine Detaille , Augusto C. Ponce

In this paper, we study the multifractal Hausdorff and packing dimensions of Borel probability measures and study their behaviors under orthogonal projections. In particular, we try through these results to improve the main result of M. Dai…

Metric Geometry · Mathematics 2019-11-01 Bilel Selmi

We show that in any Euclidean space, an arbitrary probability measure can be reconstructed explicitly by its geometric (or spatial) distribution function. The reconstruction takes the form of a (potentially fractional) linear PDE, where the…

Statistics Theory · Mathematics 2024-04-11 Dimitri Konen

The multifractal spectrum of a Borel measure $\mu$ in $\mathbb{R}^n$ is defined as \[ f_\mu(\alpha) = \dim_H {x:\lim_{r\to 0} \frac{\log \mu(B(x,r))}{\log r}=\alpha}. \] For self-similar measures under the open set condition the behavior of…

Classical Analysis and ODEs · Mathematics 2013-03-19 Pablo Shmerkin

Tukey's depth (or halfspace depth) is a widely used measure of centrality for multivariate data. However, exact computation of Tukey's depth is known to be a hard problem in high dimensions. As a remedy, randomized approximations of Tukey's…

Machine Learning · Statistics 2025-07-08 Simon Briend , Gábor Lugosi , Roberto Imbuzeiro Oliveira