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Central limit theorems (CLTs) for high-dimensional random vectors with dimension possibly growing with the sample size have received a lot of attention in the recent times. Chernozhukov et al. (2017) proved a Berry--Esseen type result for…

Statistics Theory · Mathematics 2019-06-26 Arun Kumar Kuchibhotla , Somabha Mukherjee , Debapratim Banerjee

We prove a sequence of limiting results about weakly dependent stationary and regularly varying stochastic processes in discrete time. After deducing the limiting distribution for individual clusters of extremes, we present a new type of…

Probability · Mathematics 2017-12-05 Bojan Basrak , Hrvoje Planinic , Philippe Soulier

We prove uniform convergence results for the integrated periodogram of a weakly dependent time series, namely a law of large numbers and a central limit theorem. These results are applied to Whittle's parametric estimation. Under general…

Statistics Theory · Mathematics 2008-04-15 Jean-Marc Bardet , Paul Doukhan , José Rafael León

U-statistics play central roles in many statistical learning tools but face the haunting issue of scalability. Significant efforts have been devoted into accelerating computation by U-statistic reduction. However, existing results almost…

Methodology · Statistics 2023-06-07 Meijia Shao , Dong Xia , Yuan Zhang

This paper investigates weighted approximations for studentized $U$-statistics type processes, both with symmetric and antisymmetric kernels, only under the assumption that the distribution of the projection variate is in the domain of…

Probability · Mathematics 2007-11-12 Miklós Csörgő , Barbara Szyszkowicz , Qiying Wang

In this paper, we establish a central limit theorem for a large class of general supercritical superprocesses with spatially dependent branching mechanisms satisfying a second moment condition. This central limit theorem generalizes and…

Probability · Mathematics 2014-09-22 Yan-Xia Ren , Renming Song , Rui Zhang

Quantitative limit theorems for non-linear functionals on the Wiener space are considered. Given the possibly infinite sequence of kernels of the chaos decomposition of such a functional, an estimate for different probability distances…

Probability · Mathematics 2016-10-06 Tobias Fissler , Christoph Thaele

Adapting the spectral viewpoint suggested in Ledoux (2012) in the context of symmetric Markov diffusion generators and recently exploited in the non-diffusive setup of a Poisson random measure by D\"obler and Peccati (2017), we investigate…

Probability · Mathematics 2017-10-10 Christian Döbler , Kai Krokowski

We introduce the new notion of a conjugate weight function and provide a detailed study of this operation and its properties. Then we apply this knowledge to study classes of ultradifferentiable functions defined in terms of fast growing…

Functional Analysis · Mathematics 2026-03-31 Gerhard Schindl

In this paper we consider a branching particle system consisting of particles moving according to the Ornstein-Uhlenbeck process in R^d and undergoing a binary, supercritical branching with a constant rate \lambda>0. This system is known to…

Probability · Mathematics 2014-07-10 Radosław Adamczak , Piotr Miłoś

We study the normal approximation of functionals of Poisson measures having the form of a finite sum of multiple integrals. When the integrands are nonnegative, our results yield necessary and sufficient conditions for central limit…

Probability · Mathematics 2012-06-26 Raphael Lachieze-Rey , Giovanni Peccati

We study symmetric tensor decompositions, i.e., decompositions of the form $T = \sum_{i=1}^r u_i^{\otimes 3}$ where $T$ is a symmetric tensor of order 3 and $u_i \in \mathbb{C}^n$.In order to obtain efficient decomposition algorithms, it is…

Data Structures and Algorithms · Computer Science 2025-03-12 Pascal Koiran , Subhayan Saha

Central limit theorems are established for the sum, over a spatial region, of observations from a linear process on a $d$-dimensional lattice. This region need not be rectangular, but can be irregularly-shaped. Separate results are…

Statistics Theory · Mathematics 2016-01-07 S. N. Lahiri , Peter M. Robinson

Topological study of existing random simplicial complexes is non-trivial and has led to several seminal works. However, the applicability of such studies is limited since the randomness there is usually governed by a single parameter. With…

Probability · Mathematics 2021-02-05 Takashi Owada , Gennady Samorodnitsky , Gugan Thoppe

In this article, we propose a class of $L_q$-norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model…

Statistics Theory · Mathematics 2023-03-16 Yangfan Zhang , Runmin Wang , Xiaofeng Shao

For a unimodular random graph $(G,\rho)$, we consider deformations of its intrinsic path metric by a (random) weighting of its vertices. This leads to the notion of the conformal growth exponent of $(G,\rho)$, which is the best asymptotic…

Probability · Mathematics 2020-06-02 James R. Lee

Customers arrive at rate N times alpha on a network of N single server infinite buffer queues, choose L queues uniformly, join the shortest one, and are served there in turn at rate beta. We let N go to infinity.We prove a functional…

Probability · Mathematics 2007-05-23 Carl Graham

We prove a multivariate functional version of de Jong's CLT (1990) yielding that, given a sequence of vectors of Hoeffding-degenerate U-statistics, the corresponding empirical processes on $[0,1]$ weakly converge in the Skorohod space as…

Probability · Mathematics 2022-03-18 Christian Döbler , Mikołaj Kasprzak , Giovanni Peccati

This paper presents uniform convergence rates for kernel regression estimators, in the setting of a structural nonlinear cointegrating regression model. We generalise the existing literature in three ways. First, the domain to which these…

Statistics Theory · Mathematics 2015-05-08 James A. Duffy

Existing two-sample testing techniques, particularly those based on choosing a kernel for the Maximum Mean Discrepancy (MMD), often assume equal sample sizes from the two distributions. Applying these methods in practice can require…

Machine Learning · Statistics 2025-12-17 Aaron Wei , Milad Jalali , Danica J. Sutherland