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In this paper we study the joint distributional convergence of the largest eigenvalues of the sample covariance matrix of a $p$-dimensional time series with iid entries when $p$ converges to infinity together with the sample size $n$. We…

Probability · Mathematics 2016-08-26 Johannes Heiny , Thomas Mikosch

We consider the long-time behavior of an explicit tamed Euler scheme applied to a class of stochastic differential equations driven by additive noise, under a one-sided Lipschitz continuity condition. The setting encompasses drift…

Numerical Analysis · Mathematics 2020-10-02 Charles-Edouard Bréhier

The paper considers high frequency sampled multivariate continuous-time ARMA (MCARMA) models, and derives the asymptotic behavior of the sample autocovariance function to a normal random matrix. Moreover, we obtain the asymptotic behavior…

Statistics Theory · Mathematics 2015-08-10 Vicky Fasen

A discrete gradient model for interfaces is studied. The interaction potential is a non-convex perturbation of the quadratic gradient potential. Based on a representation for the finite volume Gibbs measure obtained via a renormalization…

Mathematical Physics · Physics 2016-03-16 Susanne Hilger

We consider random resistor networks with nodes given by a point process on $\mathbb{R}^d$ and with random conductances. The length range of the electrical filaments can be unbounded. We assume that the randomness is stationary and ergodic…

Mathematical Physics · Physics 2023-07-21 A. Faggionato

Consider a piecewise smooth expanding map of the interval possessing several invariant subintervals and the same number of ergodic absolutely continuous invariant probability measures (ACIMs). After this system is perturbed to make the…

Dynamical Systems · Mathematics 2010-11-25 Dmitry Dolgopyat , Paul Wright

The paper proves several limit theorems for linear eigenvalue statistics of overlapping Wigner and sample covariance matrices. It is shown that the covariance of the limiting multivariate Gaussian distribution is diagonalized by choosing…

Probability · Mathematics 2015-11-10 Vladislav Kargin

We investigate the asymptotic distribution of the maximum of a frequency smoothed estimate of the spectral coherence of a M-variate complex Gaussian time series with mutually independent components when the dimension M and the number of…

Statistics Theory · Mathematics 2021-07-08 Alexis Rosuel , Philippe Loubaton , Pascal Vallet

Slice Sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice Sampling's performance is highly sensitive to the…

Machine Learning · Statistics 2021-10-05 Minas Karamanis , Florian Beutler

The ergodic spectral efficiency (SE) in interference-limited multiple-input multiple-output (MIMO) downlink cellular systems is characterized based on stochastic geometry. A single user is served by using singular value decomposition…

Information Theory · Computer Science 2016-11-01 Jinseok Choi , Jeonghun Park , Brian L. Evans

This paper studies the limit of a kinetic evolution equation involving a small parameter and driven by a random process which also scales with the small parameter. In order to prove the convergence in distribution to the solution of a…

Probability · Mathematics 2021-06-28 Shmuel Rakotonirina-Ricquebourg

Extended Self-Similarity (ESS) is a widely used tool for uncovering universal power-law scaling in systems dominated by nonlinear interactions. This work demonstrates that ESS scaling can also emerge in a system governed by purely linear…

Optics · Physics 2026-01-27 Mengxin Wu , Ziye Chen , Guang Yang , Mingshu Zhao

This paper is to study a signal-plus-noise model in high dimensional settings when the dimension and the sample size are comparable. Specifically, we assume that the noise has a general covariance matrix that allows for heteroskedasticity,…

Statistics Theory · Mathematics 2025-05-13 Xiaoyu Liu , Yiming Liu , Guangming Pan , Lingyue Zhang , Zhixiang Zhang

In this paper, our objective is to present a constraining principle governing the spectral properties of the sample covariance matrix. This principle exhibits harmonious behavior across diverse limiting frameworks, eliminating the need for…

Statistics Theory · Mathematics 2024-01-03 Yanqing Yin

Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned by ADVI are forced to be unimodal in order to facilitate use…

Machine Learning · Computer Science 2020-06-25 Warren R. Morningstar , Sharad M. Vikram , Cusuh Ham , Andrew Gallagher , Joshua V. Dillon

Support vector machines (SVMs) are a standard tool for binary classification, but their classical formulations are purely data-driven and offer no direct way to encode trusted benchmark models or structured preferences on selected subsets…

Machine Learning · Statistics 2026-04-29 Mohammad Jafari Jozani , Bahram Moeinianfar

An alternative to conventional uniform sampling is that of time encoding, which converts continuous-time signals into streams of trigger times. This gives rise to Event-Driven Sampling (EDS) models. The data-driven nature of EDS acquisition…

Information Theory · Computer Science 2022-10-11 Dorian Florescu , Ayush Bhandari

We study a subsystem of an isolated one-dimensional correlated metal when it is driven by a steady electric field or when it relaxes after driving. We obtain numerically exact reduced density matrix $\rho$ for subsystems which are…

Strongly Correlated Electrons · Physics 2013-05-21 M. Mierzejewski , T. Prosen , D. Crivelli , P. Prelovsek

We prove the existence of the limiting spectral distribution (LSD) of symmetric triangular patterned matrices and also establish the joint convergence of sequences of such matrices. For the particular case of the symmetric triangular Wigner…

Probability · Mathematics 2012-04-12 Riddhipratim Basu , Arup Bose , Shirshendu Ganguly , Rajat Subhra Hazra

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as powerful tools for generative modeling. However, their sequential computation requirements lead to significant inference-time bottlenecks. In this work, we utilize the…

Machine Learning · Computer Science 2025-08-08 Hengyuan Hu , Aniket Das , Dorsa Sadigh , Nima Anari