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Classical multidimensional scaling is a widely used method in dimensionality reduction and manifold learning. The method takes in a dissimilarity matrix and outputs a low-dimensional configuration matrix based on a spectral decomposition.…

Methodology · Statistics 2019-05-15 Gongkai Li , Minh Tang , Nichlas Charon , Carey E Priebe

The applicability conditions of a recently reported Central Limit Theorem-based approximation method in statistical physics are investigated and rigorously determined. The failure of this method at low and intermediate temperature is proved…

Statistical Mechanics · Physics 2012-03-21 Bruno Leggio , Oleg Lychkovskiy , Antonino Messina

The theory of random matrices contains many central limit theorems. We have central limit theorems for eigenvalues statistics, for the log-determinant and log-permanent, for limiting distribution of individual eigenvalues in the bulk, and…

Probability · Mathematics 2016-05-25 Asaf Ferber , Daniel Montealegre , Van Vu

Fr\'echet means of samples from a probability measure $\mu$ on any smoothly stratified metric space M with curvature bounded above are shown to satisfy a central limit theorem (CLT). The methods and results proceed by introducing and…

Probability · Mathematics 2023-11-17 Jonathan C. Mattingly , Ezra Miller , Do Tran

Concentration inequalities, which have proved very useful in a variety of fields, provide fairly tight bounds on large deviation probabilities while central limit theorem (CLT) describes the asymptotic distribution around the mean (at the…

Probability · Mathematics 2026-02-02 Changqing Liu

We consider the existence of the integrated density of states (IDS) of the Anderson model on the Hilbert space $\ell^2(\mathbb{Z}^d)$ as analogues to the law of large numbers (LLN). In this work, we prove the analogues central limit theorem…

Mathematical Physics · Physics 2024-12-04 Dhriti Ranjan Dolai

We give a new proof of the classical Central Limit Theorem, in the Mallows ($L^r$-Wasserstein) distance. Our proof is elementary in the sense that it does not require complex analysis, but rather makes use of a simple subadditive inequality…

Probability · Mathematics 2007-06-13 Oliver Johnson , Richard Samworth

We consider a branching random walk on $d$-dimensional real space with immigration in a time-dependent random environment. Let $Z_n(\mathbf t)$ be the so-called partition function of the process, namely, the moment generating function of…

Probability · Mathematics 2022-10-18 Chunmao Huang , Yukun Ren , Runze Li

We prove a Functional Central Limit Theorem for the position of a Tagged Particle in the one-dimensional Asymmetric Simple Exclusion Process in the hyperbolic scaling, starting from a Bernoulli product measure conditioned to have a particle…

Probability · Mathematics 2007-05-23 Patricia Goncalves

The distribution of a Markov process with killing, conditioned to be still alive at a given time, can be approximated by a Fleming-Viot type particle system. In such a system, each particle is simulated independently according to the law of…

Probability · Mathematics 2017-09-21 Frederic Cerou , Bernard Delyon , Arnaud Guyader , Mathias Rousset

Let $\{X_k\}_{k \in \mathbb{Z}}$ be a stationary Gaussian process with values in a separable Hilbert space $\mathcal{H}_1$, and let $G:\mathcal{H}_1 \to \mathcal{H}_2$ be an operator acting on $X_k$. Under suitable conditions on the…

Probability · Mathematics 2024-05-21 Marie-Christine Düker , Pavlos Zoubouloglou

We establish a central limit theorem and prove a moderate deviation principle for stochastic scalar conservation laws. Due to the lack of viscous term, this is done in the framework of kinetic solution. The weak convergence method and…

Probability · Mathematics 2022-08-31 Zhengyan Wu , Rangrang Zhang

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

Given a bounded operator $T$ on a Banach space $X$, we study the existence of a probability measure $\mu$ on $X$ such that, for many functions $f:X\to\mathbb K$, the sequence $(f+\dots+f\circ T^{n-1})/\sqrt n$ converges in distribution to a…

Functional Analysis · Mathematics 2013-04-10 Frédéric Bayart

We prove central limit theorems (CLTs) for topological functionals of Bernoulli bond percolation on infinite graphs beyond the Euclidean lattice $\mathbb{Z}^{d}$. For quasi-transitive graphs of subexponential growth, we show that the number…

Probability · Mathematics 2026-04-10 Luciano H. L. de Araújo , Daniel Miranda Machado , Cristian F. Coletti

Sample covariance matrix and multivariate $F$-matrix play important roles in multivariate statistical analysis. The central limit theorems {\sl (CLT)} of linear spectral statistics associated with these matrices were established in Bai and…

Statistics Theory · Mathematics 2013-05-03 Shurong Zheng , Zhidong Bai

Local increases in the mean of a random field are detected (conservatively) by thresholding a field of test statistics at a level $u$ chosen to control the tail probability or $p$-value of its maximum. This $p$-value is approximated by the…

Statistics Theory · Mathematics 2008-11-06 N. Chamandy , K. J. Worsley , J. Taylor , F. Gosselin

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 present an adaptation of Stein's method of normal approximation to the study of both discrete- and continuous-time dynamical systems. We obtain new correlation-decay conditions on dynamical systems for a multivariate central limit…

Probability · Mathematics 2017-01-12 Olli Hella , Juho Leppänen , Mikko Stenlund

Under the sublinear expectation $\mathbb{E}[\cdot]:=\sup_{\theta\in \Theta} E_\theta[\cdot]$ for a given set of linear expectations $\{E_\theta: \theta\in \Theta\}$, we establish a new law of large numbers and a new central limit theorem…

Probability · Mathematics 2018-05-16 Xiao Fang , Shige Peng , Qi-Man Shao , Yongsheng Song
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