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This paper presents an extensive survey of regular distributions in natural and social sciences. The survey includes studies from a wide scope of academic disciplines, in order to create an inventory of the different mathematical functions…

Physics and Society · Physics 2015-07-14 L. Benguigui , M. Marinov

We use a macroscopic Hamiltonian approach to study the pinning of a solid--liquid--vapour contact line on an array of equidistant stripes of obstacles perpendicular to the liquid. We propose an estimate of the density of pinning stripes for…

Condensed Matter · Physics 2009-10-28 J. V. Andersen , Y. Brechet

In this article, I conduct a textual and contextual analysis of the empirical literature on Zipf's law for cities. Building on previous meta-analysis material openly available, I collect full texts and bibliographies of 66 scientific…

Physics and Society · Physics 2022-02-01 Clémentine Cottineau

A normalizing flow models a complex probability density as an invertible transformation of a simple density. The invertibility means that we can evaluate densities and generate samples from a flow. In practice, autoregressive flow-based…

Machine Learning · Statistics 2019-06-06 Conor Durkan , Artur Bekasov , Iain Murray , George Papamakarios

We study spike-and-slab priors for generalized linear models with possible grouped sparsity. The main result is an oracle Bernstein--von Mises theorem for the fractional posterior under supportwise likelihood assumptions. The proof develops…

Statistics Theory · Mathematics 2026-05-27 Hanqing Li , Xuewen Lu

We propose an approach to approximate the boundary crossing probabilities for general one-dimensional diffusion processes, and derive the convergence rate for this approximation scheme. There results are based on the explicit expression of…

Probability · Mathematics 2015-10-28 Jinghai Shao , Liqun Wang

In this paper we establish a multivariate exchangeable pairs approach within the framework of Stein's method to assess distributional distances to potentially singular multivariate normal distributions. By extending the statistics into a…

Probability · Mathematics 2010-04-06 Gesine Reinert , Adrian Röllin

Mean density of lower dimensional random closed sets, as well as the mean boundary density of full dimensional random sets, and their estimation are of great interest in many real applications. Only partial results are available so far in…

Statistics Theory · Mathematics 2014-02-05 Elena Villa

We propose a new approach towards approximating the density-to-pair-density map based on copula theory from statistics. We extend the copula theory to multi-dimensional marginals, and deduce that one can describe any (exact or approximate)…

Computational Physics · Physics 2025-03-11 Geneviève Dusson , Claudia Klüppelberg , Gero Friesecke

We propose improvements in numerical evaluation of symmetric stable density and its partial derivatives with respect to the parameters. They are useful for more reliable evaluation of maximum likelihood estimator and its standard error.…

Statistics Theory · Mathematics 2007-06-13 Muneya Matsui , Akimichi Takemura

Let $\a$ be a complex random variable with mean zero and bounded variance $\sigma^{2}$. Let $N_{n}$ be a random matrix of order $n$ with entries being i.i.d. copies of $\a$. Let $\lambda_{1}, ..., \lambda_{n}$ be the eigenvalues of…

Probability · Mathematics 2008-02-29 Terence Tao , Van Vu

In this paper we study the asymptotic normality in high-dimensional linear regression. We focus on the case where the covariance matrix of the regression variables has a KMS structure, in asymptotic settings where the number of predictors,…

Statistics Theory · Mathematics 2022-05-17 Saulius Jokubaitis , Remigijus Leipus

We introduce the beta generalized normal distribution which is obtained by compounding the beta and generalized normal [Nadarajah, S., A generalized normal distribution, \emph{Journal of Applied Statistics}. 32, 685--694, 2005]…

Statistics Theory · Mathematics 2022-06-06 R. J. Cintra , L. C. Rêgo , G. M. Cordeiro , A. D. C. Nascimento

$\alpha$-posteriors and their variational approximations distort standard posterior inference by downweighting the likelihood and introducing variational approximation errors. We show that such distortions, if tuned appropriately, reduce…

Machine Learning · Statistics 2021-04-20 Marco Avella Medina , José Luis Montiel Olea , Cynthia Rush , Amilcar Velez

The Hellinger distance between quantum states is a significant measure in quantum information theory, known for its Riemannian and monotonic properties. It is also easier to compute than the Bures distance, another measure that shares these…

Quantum Physics · Physics 2024-09-24 Vinay Kumar , Kaushik Vasan , Santosh Kumar

We study the limiting distribution of critical points and extrema of random spherical harmonics, in the high energy limit. In particular, we first derive the density functions of extrema and saddles; we then provide analytic expressions for…

Mathematical Physics · Physics 2018-01-09 Valentina Cammarota , Domenico Marinucci , Igor Wigman

The distribution function of the sum $Z$ of two standard normally distributed random variables $X$ and $Y$ is computed with the concept of copulas to model the dependency between $X$ and $Y$. By using implicit copulas such as the Gauss- or…

Computation · Statistics 2021-07-02 Walter Schneider

We establish the limiting distribution of $\frac{{(\log \log x)}^{1/4}}{\sqrt{x}} \sum_{n\le x}\alpha(n)$ where $\alpha$ is a Steinhaus random multiplicative function, answering a question of Harper. The distributional convergence is proved…

Number Theory · Mathematics 2025-09-16 Ofir Gorodetsky , Mo Dick Wong

We develop the theoretical basis for the connection of the variety of anisotropic distributions with the statistical correlations among particles velocity components. By examining the most common anisotropic distribution, we derive the…

Statistical Mechanics · Physics 2021-03-17 George Livadiotis , George Nicolaou , Frederic Allegrini

By a mixture density is meant a density of the form $\pi_{\mu}(\cdot)=\int\pi_{\theta}(\cdot)\times\mu(d\theta)$, where $(\pi_{\theta})_{\theta\in\Theta}$ is a family of probability densities and $\mu$ is a probability measure on $\Theta$.…

Statistics Theory · Mathematics 2016-08-16 François Roueff , Tobias Rydén