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The generalized circumradius of a set of points $A \subseteq \mathbb{R}^d$ with respect to a convex body $K$ equals the minimum value of $\lambda \geq 0$ such that $A$ is contained in a translate of $\lambda K$. Each choice of $K$ gives a…

Metric Geometry · Mathematics 2023-02-02 David Bryant , Katharina T. Huber , Vincent Moulton , Paul F. Tupper

We present an optimal transport framework for performing regression when both the covariate and the response are probability distributions on a compact Euclidean subset $\Omega\subset\mathbb{R}^d$, where $d>1$. Extending beyond compactly…

Statistics Theory · Mathematics 2024-03-05 Laya Ghodrati , Victor M. Panaretos

We consider the moment space $\mathcal{M}_n$ corresponding to $p \times p$ real or complex matrix measures defined on the interval $[0,1]$. The asymptotic properties of the first $k$ components of a uniformly distributed vector $(S_{1,n},…

Probability · Mathematics 2011-05-18 Jan Nagel , Holger Dette

Let $G_1,\dots,G_m$ be independent copies of the standard gaussian random vector in $\mathbb{R}^d$. We show that there is an absolute constant $c$ such that for any $A \subset S^{d-1}$, with probability at least $1-2\exp(-c\Delta m)$, for…

Probability · Mathematics 2024-11-14 Daniel Bartl , Shahar Mendelson

This paper derives a new strong Gaussian approximation bound for the sum of independent random vectors. The approach relies on the optimal transport theory and yields \textit{explicit} dependence on the dimension size $p$ and the sample…

Statistics Theory · Mathematics 2021-09-06 Nazar Buzun , Nikolay Shvetsov , Dmitry V. Dylov

Let $X$ be a symmetric, isotropic random vector in $\mathbb{R}^m$ and let $X_1...,X_n$ be independent copies of $X$. We show that under mild assumptions on $\|X\|_2$ (a suitable thin-shell bound) and on the tail-decay of the marginals…

Functional Analysis · Mathematics 2022-07-13 Daniel Bartl , Shahar Mendelson

The following anticoncentration property is proved. The probability that the $k$-order statistic of an arbitrarily correlated jointly Gaussian random vector $X$ with unit variance components lies within an interval of length $\varepsilon$…

Statistics Theory · Mathematics 2021-07-23 Damian Kozbur

In this paper we propose a general methodology, based on multiple testing, for testing that the mean of a Gaussian vector in R^n belongs to a convex set. We show that the test achieves its nominal level, and characterize a class of vectors…

Statistics Theory · Mathematics 2007-06-13 Yannick Baraud , Sylvie Huet , Beatrice Laurent

In this work we study the estimation of the density of a totally positive random vector. Total positivity of the distribution of a random vector implies a strong form of positive dependence between its coordinates and, in particular, it…

Statistics Theory · Mathematics 2023-05-10 Ali Zartash , Elina Robeva

We say that $E$ is a microset of the compact set $K\subset \mathbb{R}^d$ if there exist sequences $\lambda_n\geq 1$ and $u_n\in \mathbb{R}^d$ such that $(\lambda_n K + u_n ) \cap [0,1]^d$ converges to $E$ in the Hausdorff metric, and…

Classical Analysis and ODEs · Mathematics 2021-04-21 Richárd Balka , Márton Elekes , Viktor Kiss

The $K$-hull of a compact set $A\subset\mathbb{R}^d$, where $K\subset \mathbb{R}^d$ is a fixed compact convex body, is the intersection of all translates of $K$ that contain $A$. A set is called $K$-strongly convex if it coincides with its…

Metric Geometry · Mathematics 2021-10-06 Alexander Marynych , Ilya Molchanov

Consider the problem of estimating the mean of a Gaussian random vector when the mean vector is assumed to be in a given convex set. The most natural solution is to take the Euclidean projection of the data vector on to this convex set; in…

Statistics Theory · Mathematics 2014-11-21 Sourav Chatterjee

Computation of the vertices of the convex hull of a set $S$ of $n$ points in $\mathbb{R} ^m$ is a fundamental problem in computational geometry, optimization, machine learning and more. We present "All Vertex Triangle Algorithm" (AVTA), a…

Computational Geometry · Computer Science 2018-09-26 Pranjal Awasthi , Bahman Kalantari , Yikai Zhang

The variety of consistent "gauging" deformations of supergravity theories in four dimensions depends on the choice of Lagrangian formulation. One important goal is to get the most general deformations without making hidden assumptions.…

High Energy Physics - Theory · Physics 2018-02-14 Marc Henneaux , Bernard Julia , Victor Lekeu , Arash Ranjbar

This paper studies the problem of estimating the covariance of a collection of vectors using only highly compressed measurements of each vector. An estimator based on back-projections of these compressive samples is proposed and analyzed. A…

Machine Learning · Statistics 2019-01-16 Martin Azizyan , Akshay Krishnamurthy , Aarti Singh

For a given statistic, A, the cosmic distribution function, Upsilon(VA), is the probability of measuring a value VA in a finite galaxy catalog. For statistics related to count-in-cells, such as factorial moments, F_k, the average…

Astrophysics · Physics 2007-05-23 S. Colombi , I. Szapudi , the VIRGO consortium

Let $K$ be an isotropic symmetric convex body in ${\mathbb R}^n$. We show that a subspace $F\in G_{n,n-k}$ of codimension $k=\gamma n$, where $\gamma\in (1/\sqrt{n},1)$, satisfies $$K\cap F\subseteq \frac{c}{\gamma }\sqrt{n}L_K (B_2^n\cap…

Metric Geometry · Mathematics 2016-09-29 Apostolos Giannopoulos , Labrini Hioni , Antonis Tsolomitis

We prove bounds for the number of solutions to $$a_1 + \dots + a_k = a_1' + \dots + a_k'$$ over $N$-element sets of reals, which are sufficiently convex or near-convex. A near-convex set will be the image of a set with small additive…

Number Theory · Mathematics 2021-04-26 Peter J. Bradshaw , Brandon Hanson , Misha Rudnev

We prove the four-dimensional Gaussian random vector maximum conjecture. This conjecture asserts that among all centered Gaussian random vectors $X=(X_1,X_2,X_3,X_4)$ with $E[X_i^2]=1$, $1\le i\le 4$, the expectation…

Probability · Mathematics 2020-08-18 Wei Sun , Ze-Chun Hu , Guolie Lan

Consider a measurable space with an atomless finite vector measure. This measure defines a mapping of the $\sigma$-field into an Euclidean space. According to the Lyapunov convexity theorem, the range of this mapping is a convex compactum.…

Probability · Mathematics 2011-02-14 Peng Dai , Eugene A. Feinberg