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This paper examines the foundational concept of random variables in probability theory and statistical inference, demonstrating that their mathematical definition requires no reference to randomization or hypothetical repeated sampling. We…

Other Statistics · Statistics 2025-02-11 Paul W. Vos

For independent random variables $X_1,\ldots, X_n;Y_1,\ldots, Y_n$ with all $X_i$ identically distributed and same for $Y_j$, we study the relation \[E\{a\bar X + b\bar Y|X_1 -\bar X +Y_1 -\bar Y,\ldots,X_n -\bar X +Y_n -\bar Y\}={\rm…

Statistics Theory · Mathematics 2019-02-20 Abram M. Kagan

Along the line of thoughts of Berry and Robnik{\cite{Ber}}, the limiting gap distribution function of classically integrable quantum systems is derived in the limit of infinitely many independent components. The limiting gap distribution…

Chaotic Dynamics · Physics 2007-05-23 H. Makino , S. Tasaki

Student's theorem is an important result in statistics which states that for normal population, the sample variance is independent from the sample mean and has a chi-square distribution. The existing proofs of this theorem either overly…

Other Statistics · Statistics 2018-06-22 Yiping Cheng

We derive the distribution of the eigenvalues of a large sample covariance matrix when the data is dependent in time. More precisely, the dependence for each variable $i=1,...,p$ is modelled as a linear process…

Probability · Mathematics 2012-01-19 Oliver Pfaffel , Eckhard Schlemm

In this paper, we discuss the joint value distribution of $L$-functions in a suitable class. We obtain joint large deviations results in the central limit theorem for these $L$-functions and some mean value theorems, which give evidence…

Number Theory · Mathematics 2021-02-26 Shōta Inoue , Junxian Li

Limit theorems for non-additive probabilities or non-linear expectations are challenging issues which have raised progressive interest recently. The purpose of this paper is to study the strong law of large numbers and the law of the…

Probability · Mathematics 2016-08-03 Li-Xin Zhang

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

We consider a random symmetric matrix ${\bf X} = [X_{jk}]_{j,k=1}^n$ with upper triangular entries being independent identically distributed random variables with mean zero and unit variance. We additionally suppose that $\mathbb E…

Probability · Mathematics 2016-12-01 Friedrich Götze , Alexey Naumov , Alexander Tikhomirov

The well-known Heyde theorem characterizes the Gaussian distributions on the real line by the symmetry of the conditional distribution of one linear form of independent random variables given another. We generalize this theorem to groups of…

Probability · Mathematics 2024-05-31 Gennadiy Feldman

We investigate conditions for the existence of the limiting conditional distribution of a bivariate random vector when one component becomes large. We revisit the existing literature on the topic, and present some new sufficient conditions.…

Probability · Mathematics 2010-02-21 Anne-Laure Fougères , Philippe Soulier

Thurstone's latent-normal model, introduced a century ago to describe human preferences in psychometrics (1927), remains a cornerstone for modeling random rankings. Yet when the underlying normals differ in distribution, the joint law of…

Statistics Theory · Mathematics 2025-08-01 Philip T. Labo

We wish to test whether a real-valued variable $Z$ has explanatory power, in addition to a multivariate variable $X$, for a binary variable $Y$. Thus, we are interested in testing the hypothesis $\mathbb{P}(Y=1\, | \, X,Z)=\mathbb{P}(Y=1\,…

Methodology · Statistics 2025-12-23 John H. J. Einmahl , Denis Kojevnikov , Bas J. M. Werker

The Lov\'{a}sz Local Lemma (LLL) is a keystone principle in probability theory, guaranteeing the existence of configurations which avoid a collection $\mathcal B$ of "bad" events which are mostly independent and have low probability. In its…

Data Structures and Algorithms · Computer Science 2019-09-20 David G. Harris

Let $T\$ be a stopping time associated with a sequence of independent random variables $Z_{1},Z_{2},...$ . By applying a suitable change in the probability measure we present relations between the moment or probability generating functions…

Statistics Theory · Mathematics 2011-06-28 M. V. Boutsikas , A. C. Rakitzis , D. L. Antzoulakos

Beginning with work of Zeilberger on classical pattern counts, there are a variety of structural results for moments of permutation statistics applied to random permutations. Using tools from representation theory, Gaetz and Ryba…

Combinatorics · Mathematics 2025-03-25 Zachary Hamaker , Brendon Rhoades

We study the relationship between the well-known Carleman's condition guaranteeing that a probability distribution is uniquely determined by its moments, and a recent easily checkable condition on the rate of growth of the moments. We use…

Probability · Mathematics 2019-12-03 Elena B. Yarovaya , Jordan M. Stoyanov , Konstantin K. Kostyashin

For $g < n$, let $b\_1,...,b\_{n-g}$ be $n - g$ independent vectors in $\mathbb{R}^n$ with a common distribution invariant by rotation. Considering these vectors as a basis for the Euclidean lattice they generate, the aim of this paper is…

Probability · Mathematics 2007-05-23 Ali Akhavi , Jean-François Marckert , Alain Rouault

The classical theorem of Wendel provides an exact formula for the probability that the convex hull of independent symmetrically distributed vectors in ${\mathbb R}^d$ contains the origin as long as the distributions of the vectors are…

Metric Geometry · Mathematics 2025-08-12 Konstantin Tikhomirov

Let $(\xi_1, \eta_1)$, $(\xi_2, \eta_2),\ldots$ be independent identically distributed $\mathbb{N}^2$-valued random vectors with arbitrarily dependent components. The sequence $(\Theta_k)_{k\in\mathbb{N}}$ defined by…

Probability · Mathematics 2023-10-10 Victor Bohdanskyi , Vladyslav Bohun , Alexander Marynych , Igor Samoilenko