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We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those…

Statistics Theory · Mathematics 2024-11-08 Natalie Neumeyer , Leonie Selk

The family of skew-symmetric distributions is a wide set of probability density functions obtained by combining in a suitable form a few components which are selectable quite freely provided some simple requirements are satisfied. Intense…

Probability · Mathematics 2010-12-22 Adelchi Azzalini , Giuliana Regoli

The earlier work of the author on Frequency estimation in three-phase power systems ( that is included as the reference number 1) is expanded to the distributed setting in order present a framework for the implementation of such a frequency…

Applications · Statistics 2016-03-31 Sayed Pouria Talebi

We discuss the asymptotic behaviour of models of lattice polygons, mainly on the square lattice. In particular, we focus on limiting area laws in the uniform perimeter ensemble where, for fixed perimeter, each polygon of a given area occurs…

Mathematical Physics · Physics 2014-12-22 Christoph Richard

Inspired by recent use of polarimetry to study the Cosmic Microwave Background and extragalatic supernovae, a foray into the statistical properties of Stokes parameters expressed in spherical coordinates is began, allowing circular…

Instrumentation and Methods for Astrophysics · Physics 2014-11-19 Jason L. Quinn

Applications of harmonic analysis on finite groups were recently introduced to measure partition problems, with a variety of equipartition types by convex fundamental domains obtained as the vanishing of prescribed Fourier transforms.…

Metric Geometry · Mathematics 2015-11-10 Steven Simon

This paper considers the topic of finding prior distributions when a major component of the statistical model depends on a nonlinear function. Using results on how to construct uniform distributions in general metric spaces, we propose a…

Computation · Statistics 2014-05-09 Björn Bornkamp

We compute the full probability distribution of the spectral form factor in the self-dual kicked Ising model by providing an exact lower bound for each moment and verifying numerically that the latter is saturated. We show that at large…

Chaotic Dynamics · Physics 2021-01-04 Ana Flack , Bruno Bertini , Tomaz Prosen

We describe a geometric method to quantify wave patterns observed in the nervous system, which are non-stationary and with a mixture of spiral, target, plane and irregular waves. The method analyzes fluctuations of the energy angular…

Pattern Formation and Solitons · Physics 2015-03-13 Chuan Zhang , Kentaroh Takagaki , Xiaoying Huang , Steven J. Schiff , Jian-Young Wu

The purpose of this work is to find the time dependent distributions of directions and positions of a particle that undergoes multiple elastic scattering. The angular cross section is given and the scatterers are randomly placed. The…

Disordered Systems and Neural Networks · Physics 2007-05-23 Ricardo Garcia-Pelayo

Symmetry plays a central role in the sciences, machine learning, and statistics. For situations in which data are known to obey a symmetry, a multitude of methods that exploit symmetry have been developed. Statistical tests for the presence…

Methodology · Statistics 2024-12-24 Kenny Chiu , Benjamin Bloem-Reddy

Consider a nonparametric regression model with one-sided errors and regression function in a general H\"older class. We estimate the regression function via minimization of the local integral of a polynomial approximation. We show uniform…

Methodology · Statistics 2016-10-12 Holger Drees , Natalie Neumeyer , Leonie Selk

We introduce the notion of symmetric covariation, which is a new measure of dependence between two components of a symmetric $\alpha$-stable random vector, where the stability parameter $\alpha$ measures the heavy-tailedness of its…

Statistics Theory · Mathematics 2021-05-20 Yujia Ding , Qidi Peng

Local Fourier analysis is a strong and well-established tool for analyzing the convergence of numerical methods for partial differential equations. The key idea of local Fourier analysis is to represent the occurring functions in terms of a…

Numerical Analysis · Mathematics 2015-03-12 Stefan Takacs

We propose the Fourier-domain transfer entropy spectrum, a novel generalization of transfer entropy, as a model-free metric of causality. For arbitrary systems, this approach systematically quantifies the causality among their different…

Data Analysis, Statistics and Probability · Physics 2021-10-14 Yang Tian , Yaoyuan Wang , Ziyang Zhang , Pei Sun

Many examples of natural systems can be described by random Gaussian surfaces. Much can be learned by analyzing the Fourier expansion of the surfaces, from which it is possible to determine the corresponding Hurst exponent and consequently…

Statistical Mechanics · Physics 2017-09-01 C. P. de Castro , M. Lukovic , R. F. S. Andrade , H. J. Herrmann

We study the universal nature of global fluctuations in the critical regime of the spherical model by evaluating the exact distribution of the magnetization and its absolute value in the thermodynamical limit, in the presence of a conjugate…

Statistical Mechanics · Physics 2015-06-11 Jean-Yves Fortin , Sophie Mantelli

The scattering of free particles constrained to move on a cylindrically symmetric curved surface is studied. The nontrivial geometry of the space contributes to the scattering cross section through the kinetic as well as a possible scalar…

High Energy Physics - Theory · Physics 2009-10-30 Ali Mostafazadeh

Products between phase-type distributed random variables and any independent, positive and continuous random variable are studied. Their asymptotic properties are established, and an expectation-maximization algorithm for their effective…

Probability · Mathematics 2021-11-25 Hansjoerg Albrecher , Martin Bladt , Mogens Bladt , Jorge Yslas

Probabilistic modeling is cyclical: we specify a model, infer its posterior, and evaluate its performance. Evaluation drives the cycle, as we revise our model based on how it performs. This requires a metric. Traditionally, predictive…

Machine Learning · Statistics 2016-05-25 Alp Kucukelbir , David M. Blei
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