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Increasingly, statisticians are faced with the task of analyzing complex data that are non-Euclidean and specifically do not lie in a vector space. To address the need for statistical methods for such data, we introduce the concept of…

Methodology · Statistics 2017-10-05 Alexander Petersen , Hans-Georg Müller

We study the distribution of the angles between Oseledets subspaces and their log-integrability, focusing on dimension $2$. For random i.i.d. products of matrices, we construct examples of probability measures on $\mathrm{GL}_2(\mathbb{R})$…

Dynamical Systems · Mathematics 2025-12-02 Jairo Bochi , Pablo Lessa

The interpretation proposed in quant-ph/9812011 is extended to the general case of a non-relativistic particle moving in an arbitrary external potential. It is shown that, even in this general case, "particle" solutions exist which do not…

Quantum Physics · Physics 2007-05-23 A. Raiteri

The Clifford spectrum is a form of joint spectrum for noncommuting matrices. This theory has been applied in photonics, condensed matter and string theory. In applications, the Clifford spectrum can be efficiently approximated using…

Operator Algebras · Mathematics 2023-11-30 Alexander Cerjan , Terry A. Loring

The asymptotic concentration of the Fr{\'e}chet mean of IID random variables on a Rieman-nian manifold was established with a central limit theorem by Bhattacharya \& Patrangenaru (BP-CLT) [6]. This asymptotic result shows that the…

Differential Geometry · Mathematics 2019-06-19 Xavier Pennec

We establish a central limit theorem (CLT) for families of products of $\epsilon$-independent random variables. We utilize graphon limits to encode the evolution of independence and characterize the limiting distribution. Our framework…

Probability · Mathematics 2025-04-15 Guillaume Cébron , Patrick Oliveira Santos , Pierre Youssef

We propose an axiomatic approach to the concept of an intrinsic dimension of a dataset, based on a viewpoint of geometry of high-dimensional structures. Our first axiom postulates that high values of dimension be indicative of the presence…

Machine Learning · Computer Science 2016-11-17 Vladimir Pestov

We investigate isoperimetric inequalities for Lipschitz 2-spheres in CAT(0) spaces, proving bounds on the volume of efficient null-homotopies. In one dimension lower, it is known that a quadratic inequality with a constant smaller than…

Metric Geometry · Mathematics 2025-02-06 Cornelia Druţu , Urs Lang , Panos Papasoglu , Stephan Stadler

In the present paper, we study central limit theorems (CLTs) for non-symmetric random walks on nilpotent covering graphs from a point of view of discrete geometric analysis developed by Kotani and Sunada. We establish a semigroup CLT for a…

Probability · Mathematics 2021-08-17 Satoshi Ishiwata , Hiroshi Kawabi , Ryuya Namba

We obtain the law of large numbers (LLN) and the central limit theorem (CLT) for weakly dependent non-stationary arrays of random fields with asymptotically unbounded moments. The weak dependence condition for arrays of random fields is…

Statistics Theory · Mathematics 2024-08-15 Yue Pan , Jiazhu Pan

The existing Fr\'echet regression is actually defined within a linear framework, since the weight function in the Fr\'echet objective function is linearly defined, and the resulting Fr\'echet regression function is identified to be a linear…

Methodology · Statistics 2024-03-28 Lu Lin , Ze Chen

In this paper, we consider the essential spectrum of submanifolds in Euclidean spaces under various geometric hypotheses. Our results involve extrinsic conditions such as finite total mean curvature, the convergence of the gradient of the…

Differential Geometry · Mathematics 2026-05-21 Yuxin Dong , Hezi Lin , Wei Zhang

Zeitlin's model is a spatial discretization for the 2-D Euler equations on the flat 2-torus or the 2-sphere. Contrary to other discretizations, it preserves the underlying geometric structure, namely that the Euler equations describe…

Differential Geometry · Mathematics 2025-08-12 Klas Modin , Stephen C. Preston

Finite Sample Smeariness (FSS) has been recently discovered. It means that the distribution of sample Fr\'echet means of underlying rather unsuspicious random variables can behave as if it were smeary for quite large regimes of finite…

Statistics Theory · Mathematics 2021-03-02 Benjamin Eltzner , Shayan Hundrieser , Stephan F. Huckemann

We describe a very general (nonlinear) Fredholm theory for a new class of ambient spaces, called polyfolds. The basic feature of these new spaces is that in general they may have locally varying dimensions. These new spaces are needed for a…

Functional Analysis · Mathematics 2008-10-07 Helmut Hofer , Kris Wysocki , Eduard Zehnder

We use a new method to give conditions for the existence of a local isometric immersion of a Riemannian $n$-manifold $M$ in $\mathbb{R}^{n+k}$, for a given $n$ and $k$. These equate to the (local) existence of a $k$-tuple of scalar fields…

Differential Geometry · Mathematics 2019-09-02 Dan Gregorian Fodor

This paper deals with the subject of infinitesimal variations of Euclidean submanifolds with arbitrary dimension and codimension. The main goal is to establish a Fundamental theorem for these geometric objects. Similar to the theory of…

Differential Geometry · Mathematics 2020-07-15 M. Dajczer , M. I. Jimenez

In this paper we consider a superprocess being a measure-valued diffusion corresponding to the equation $u_{t}=Lu+\alpha u-\beta u^{2}$, where $L$ is the infinitesimal operator of the \emph{Ornstein-Uhlenbeck process} and…

Probability · Mathematics 2012-04-02 Piotr Miłoś

In this article we extend several foundational results of the theory of complete minimal surfaces of finite index in the Euclidean space to minimal surfaces in asymptotically flat manifolds and, more generally, to marginally outer-trapped…

Differential Geometry · Mathematics 2014-04-08 Alessandro Carlotto

Under a set of assumptions on a family of submanifolds $\subset {\mathbb R}^D$, we derive a series of geometric properties that remain valid after finite-dimensional and almost isometric Diffusion Maps (DM), including almost uniform…

Machine Learning · Statistics 2026-05-15 Wenyu Bo , Marina Meilă