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A method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential equation with localized sources. The…

Data Analysis, Statistics and Probability · Physics 2019-09-10 Christopher G. Albert

We study the convergence of Bernstein type operators leading to two results. The first: The kernel $K_n$ of the Bernstein-Durrmeyer operator at each point $x \in (0, 1)$ $\unicode{x2013}$ that is $K_n(x, t) dt$ $\unicode{x2013}$ once…

Classical Analysis and ODEs · Mathematics 2023-12-05 Mohammed Taariq Mowzer

Determinantal point processes on a measure space X whose kernels represent trace class Hermitian operators on L^2(X) are associated to "quasifree" density operators on the Fock space over L^2(X).

Probability · Mathematics 2007-05-23 Alex D. Gottlieb

In this paper, we are concerned with the large N limit of linear combinations of the entries of a Brownian motion on the group of N by N unitary matrices. We prove that the process of such a linear combination converges to a Gaussian one.…

Probability · Mathematics 2011-06-22 Florent Benaych-Georges

The class of Generalized $L$-statistics ($GL$-statistics) unifies a broad class of different estimators, for example scale estimators based on multivariate kernels. $GL$-statistics are functionals of $U$-quantiles and therefore the…

Statistics Theory · Mathematics 2017-05-15 Svenja Fischer

We investigate the limiting behavior of discrete determinantal point processes (DPPs) towards continuous DPPs when the size of the set to sample from goes to infinity. We propose a non-asymptotic characterization of this limit in terms of…

Probability · Mathematics 2026-03-03 Hugo Jaquard , Nicolas Keriven

We establish a central limit theorem for the eigenvalue counting function of a matrix of real Gaussian random variables.

Probability · Mathematics 2024-03-12 Advay Goel , Patrick Lopatto , Xiaoyu Xie

We establish Gaussian limits for general measures induced by binomial and Poisson point processes in d-dimensional space. The limiting Gaussian field has a covariance functional which depends on the density of the point process. The general…

Probability · Mathematics 2007-05-23 Yu. Baryshnikov , J. E. Yukich

This note gives an explicit description of conditional measures for the determinantal point process with the Bergman kernel.

Probability · Mathematics 2022-01-03 Alexander I. Bufetov

We study the global fluctuations for a class of determinantal point processes coming from large systems of non-colliding processes and non-intersecting paths. Our main assumption is that the point processes are constructed by biorthogonal…

Mathematical Physics · Physics 2015-12-22 Maurice Duits

We consider sequences of $U$-processes based on symmetric kernels of a fixed order, that possibly depend on the sample size. Our main contribution is the derivation of a set of analytic sufficient conditions, under which the aforementioned…

Probability · Mathematics 2022-03-16 Christian Döbler , Mikołaj Kasprzak , Giovanni Peccati

We prove a convergence theorem for U-statistics of degree two, where the data dimension $d$ is allowed to scale with sample size $n$. We find that the limiting distribution of a U-statistic undergoes a phase transition from the…

Statistics Theory · Mathematics 2023-07-04 Kevin H. Huang , Xing Liu , Andrew B. Duncan , Axel Gandy

Motivated by questions in quantum theory, we study Hilbert space valued Gaussian processes, and operator-valued kernels, i.e., kernels taking values in B(H) (= all bounded linear operators in a fixed Hilbert space H). We begin with a…

Functional Analysis · Mathematics 2024-08-21 Palle E. T. Jorgensen , James Tian

This paper is concerned with the limiting spectral behaviors of large dimensional Kendall's rank correlation matrices generated by samples with independent and continuous components. We do not require the components to be identically…

Statistics Theory · Mathematics 2019-12-16 Zeng Li , Qinwen Wang , Runze Li

We consider the determinantal point process with the confluent hypergeometric kernel. This process is a universal point process in random matrix theory and describes the distribution of eigenvalues of large random Hermitian matrices near…

Mathematical Physics · Physics 2024-02-20 Shuai-Xia Xu , Shu-Quan Zhao , Yu-Qiu Zhao

We define the local empirical process, based on $n$ i.i.d. random vectors in dimension $d$, in the neighborhood of the boundary of a fixed set. Under natural conditions on the shrinking neighborhood, we show that, for these local empirical…

Statistics Theory · Mathematics 2011-04-22 John H. J. Einmahl , Estáte V. Khmaladze

Let $G$ be an $N \times N$ real matrix whose entries are independent identically distributed standard normal random variables $G_{ij} \sim \mathcal{N}(0,1)$. The eigenvalues of such matrices are known to form a two-component system…

Probability · Mathematics 2015-12-07 N. J. Simm

We consider sub-critical configuration models and show that the central limit theorem for any additive statistic holds when the statistics satisfies a fourth moment assumption, a variance lower bound and the degree sequence of graph…

Probability · Mathematics 2019-02-22 Siva Athreya , D. Yogeshwaran

Let $(Z_t^{(q, H)})_{t \geq 0}$ denote a Hermite process of order $q \geq 1$ and self-similarity parameter $H \in (\frac{1}{2}, 1)$. Consider the Hermite-driven moving average process $$X_t^{(q, H)} = \int_0^t x(t-u) dZ^{(q, H)}(u), \qquad…

Probability · Mathematics 2017-05-19 T. T. Diu Tran

Let $U$ be a random unitary matrix drawn from the Hua-Pickrell distribution $\mu_{\mathrm{U}(n+m)}^{(\delta)}$ on the unitary group $\mathrm{U}(n+m)$. We show that the eigenvalues of the truncated unitary matrix $[U_{i,j}]_{1\leq i,j\leq…

Probability · Mathematics 2022-05-17 Zhaofeng Lin , Yanqi Qiu , Kai Wang