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We obtain large deviations estimates for both sequential and random compositions of intermittent maps. We also address the question of whether or not centering is necessary for the quenched central limit theorems (CLT) obtained by Nicol,…

Dynamical Systems · Mathematics 2020-08-14 Matthew Nicol , Felipe Perez Pereira , Andrew Torok

We prove for the rescaled convolution map $f\to f\circledast f$ propagation of polynomial, exponential and gaussian localization. The gaussian localization is then used to prove an optimal bound on the rate of entropy production by this…

Probability · Mathematics 2011-06-14 E. Carlen , A. Soffer

A law of large numbers and a central limit theorem are derived for linear statistics of random symmetric matrices whose on-or-above diagonal entries are independent, but neither necessarily identically distributed, nor necessarily all of…

Probability · Mathematics 2007-05-23 Greg Anderson , Ofer Zeitouni

This paper re-examines the limit theorems of Abadie and Imbens for nearest-neighbor matching estimators of average treatment effects with a fixed number of matches. We establish, for the first time, a non-normalized central limit theorem…

Statistics Theory · Mathematics 2026-05-21 Songliang Chen , Fang Han

The paper deals with a random connection model, a random graph whose vertices are given by a homogeneous Poisson point process on $\mathbb{R}^d$, and edges are independently drawn with probability depending on the locations of the two end…

Probability · Mathematics 2021-02-18 Van Hao Can , Khanh Duy Trinh

Motivated by open problems in applied and computational algebraic topology, we establish multivariate normal approximation theorems for three random vectors which arise organically in the study of random clique complexes. These are: (1) the…

Probability · Mathematics 2022-06-22 Tadas Temčinas , Vidit Nanda , Gesine Reinert

We provide an abstract multivariate central limit theorem with the Lindeberg-type error bounded in terms of Lipschitz functions (Wasserstein 1-distance) or functions with bounded second or third derivatives. The result is proved by means of…

Probability · Mathematics 2019-01-03 Martin Raič

We establish asymptotically Gaussian fluctuations for functionals of a large class of spin models and strongly correlated random point fields, achieving near-optimal rates. For spin models, we demonstrate Gaussian asymptotics for the…

Probability · Mathematics 2025-09-16 Tien-Cuong Dinh , Subhroshekhar Ghosh , Hoang-Son Tran , Manh-Hung Tran

This paper investigates the behavior of statistical ensembles under iteration map induced by discrete integrable Hamiltonian systems in deterministic case and stochastic case, addressing the problem from two perspectives: the Law of Large…

Probability · Mathematics 2025-09-26 Xinyu Liu , Xinze Zhang , Yong Li

We prove a central limit theorem for the length of the longest subsequence of a random permutation which follows one of a class of repeating patterns. This class includes every fixed pattern of ups and downs having at least one of each,…

Combinatorics · Mathematics 2024-09-25 Aaron Abrams , Eric Babson , Henry Landau , Zeph Landau , James Pommersheim

Using the information current, we develop a Lorentz-covariant framework for modeling equilibrium fluctuations in relativistic kinetic theory in the grand-canonical ensemble. The resulting stochastic theory is proven to be causal and…

Nuclear Theory · Physics 2024-07-24 Gabriel Soares Rocha , Lorenzo Gavassino , Nicki Mullins

For a sequence $\{X_{n}, \, n \geqslant 1 \}$ of nonnegative random variables where $\max[\min(X_{n} - s,t),0]$, $t > s \geqslant 0$, satisfy a moment inequality, sufficient conditions are given under which $\sum_{k=1}^n (X_k - \mathbb{E}…

Probability · Mathematics 2020-11-23 João Lita da Silva

We deduce sufficient conditions for the Central Limit Theorem (CLT) in the Lebesgue-Riesz space L(p) defined on some measure space for the sequence of centered random variables satisfying the strong mixing (Rosenblatt) condition. We…

Probability · Mathematics 2019-12-05 M. R. Formica , E. Ostrovsky , L. Sirota

In this article, we establish a near-optimal convergence rate for the CLT of linear eigenvalue statistics of Wigner matrices, in Kolmogorov-Smirnov distance. For all test functions $f\in C^5(\mathbb R)$, we show that the convergence rate is…

Probability · Mathematics 2021-03-22 Zhigang Bao , Yukun He

In this paper we study the frequentist convergence rate for the Latent Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum likelihood estimator converges to one of the finitely many equivalent parameters in…

Machine Learning · Statistics 2019-01-21 Yining Wang

This is the second part of study on the optimal convergence rate of the explicit Euler discretization in time for the convection-diffusion equations [Appl. Math. Lett. \textbf{131} (2022) 108048] which focuses on high-dimensional…

Numerical Analysis · Mathematics 2022-05-13 Qifeng Zhang , Jiyuan Zhang , Zhi-zhong Sun

We develop a new toolbox for the analysis of the global behavior of stochastic discrete particle systems. We introduce and study the notion of the Schur generating function of a random discrete configuration. Our main result provides a…

Probability · Mathematics 2021-01-01 Alexey Bufetov , Vadim Gorin

We establish two theorems for assessing the accuracy in total variation of multivariate discrete normal approximation to the distribution of an integer valued random vector $W$. The first is for sums of random vectors whose dependence…

Probability · Mathematics 2018-07-19 A. D. Barbour , A. Xia

In this work, we establish a Trotter-Kato type theorem. More precisely, we characterize the convergence in distribution of Feller processes by examining the convergence of their generators. The main novelty lies in providing quantitative…

Probability · Mathematics 2024-11-14 Dirk Erhard , Tertuliano Franco , Milton Jara , Eduardo Pimenta

In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regression task. The different networks are trained via different…

Machine Learning · Statistics 2024-06-14 Arnaud Descours , Tom Huix , Arnaud Guillin , Manon Michel , Éric Moulines , Boris Nectoux
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