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It is argued that there is a need for fat-tailed distributions that become thin in the extreme tail. A 3-parameter distribution is introduced that visually resembles the t-distribution and interpolates between the normal distribution and…

Statistics Theory · Mathematics 2022-02-08 Rose D Baker

Consider a probability distribution subordinate to a subexponential distribution with finite mean. In this paper, we discuss the second order tail behavior of the subordinated distribution within a rather general framework in which we do…

Probability · Mathematics 2010-11-17 Jianxi Lin

We consider random walks amongst random conductances in the cases where the conductances can be arbitrarily small, with a heavy-tailed distribution at 0, and where the conductances may or may not have a heavy-tailed distribution at…

Probability · Mathematics 2024-02-19 David A. Croydon , Daniel Kious , Carlo Scali

In this article we study the influence of regularly varying probability measures on additive and multiplicative Boolean convolutions. We introduce the notion of Boolean subexponentiality (for additive Boolean convolution), which extends the…

Probability · Mathematics 2018-08-13 Sukrit Chakraborty , Rajat Subhra Hazra

We show that, under the long-tailedness of the densities of normalized L\'evy measures, the densities of infinitely divisible distributions on the half line are subexponential if and only if the densities of their normalized L\'evy measures…

Probability · Mathematics 2019-12-19 Toshiro Watanabe

In this paper we consider a stochastic model of perpetuity-type. In contrast to the classical affine perpetuity model of Kesten [12] and Goldie [8] all discount factors in the model are mutually independent. We prove that the tails of the…

Probability · Mathematics 2017-03-22 Thomas Mikosch , Mohsen Rezapour , Olivier Wintenberger

We characterize the second order subexponentiality of an infinitely divisible distribution on the real line under an exponential moment assumption. We investigate the asymptotic behaviour of the difference between the tails of an infinitely…

Probability · Mathematics 2020-01-30 Toshiro Watanabe

We consider Bayesian inverse problems wherein the unknown state is assumed to be a function with discontinuous structure a priori. A class of prior distributions based on the output of neural networks with heavy-tailed weights is…

Machine Learning · Computer Science 2021-12-21 Chen Li , Matthew Dunlop , Georg Stadler

The (general) hypoexponential distribution is the distribution of a sum of independent exponential random variables. We consider the particular case when the involved exponential variables have distinct rate parameters. We prove that the…

Probability · Mathematics 2020-12-16 George P. Yanev

The generalized extreme value distribution and its particular case, the Gumbel extreme value distribution, are widely applied for extreme value analysis. The Gumbel distribution has certain drawbacks because it is a non-heavy-tailed…

Methodology · Statistics 2015-08-12 E. C. Pinheiro , S. L. P. Ferrari

Let $\{\xi_1,\xi_2,\ldots\}$ be a sequence of independent random variables (not necessarily identically distributed), and $\eta$ be a counting random variable independent of this sequence. We obtain sufficient conditions on…

Probability · Mathematics 2016-04-07 Svetlana Danilenko , Simona Paškauskaitė , Jonas Šiaulys

The tail of the distribution of a sum of a random number of independent and identically distributed nonnegative random variables depends on the tails of the number of terms and of the terms themselves. This situation is of interest in the…

Probability · Mathematics 2008-12-10 Christian Y. Robert , Johan Segers

We study the long-time behavior of the probability density associated with the decoupled continuous-time random walk which is characterized by a superheavy-tailed distribution of waiting times. It is shown that if the random walk is…

Statistical Mechanics · Physics 2011-05-02 S. I. Denisov , H. Kantz

This paper addresses the problem of Generalized Category Discovery (GCD) under a long-tailed distribution, which involves discovering novel categories in an unlabelled dataset using knowledge from a set of labelled categories. Existing…

Computer Vision and Pattern Recognition · Computer Science 2025-06-23 Bingchen Zhao , Kai Han

In this paper, we investigate and develop a new approach to the numerical analysis and characterization of random fluctuations with heavy-tailed probability distribution function (PDF), such as turbulent heat flow and solar flare…

Statistical Mechanics · Physics 2017-03-22 Mohsen Ghasemi Nezhadhaghighi , Abbas Nakhlband

The paper focuses on a class of light-tailed multivariate probability distributions. These are obtained via a transformation of the margins from a heavy-tailed original distribution. This class was introduced in Balkema et al. (J.…

Probability · Mathematics 2012-11-26 Guus Balkema , Paul Embrechts , Natalia Nolde

We consider a multivariate heavy-tailed stochastic volatility model and analyze the large-sample behavior of its sample covariance matrix. We study the limiting behavior of its entries in the infinite-variance case and derive results for…

Probability · Mathematics 2016-05-10 Anja Janßen , Thomas Mikosch , Mohsen Rezapour , Xiaolei Xie

The exact expression for the probability density $p_{_N}(x)$ for sums of a finite number $N$ of random independent terms is obtained. It is shown that the very tail of $p_{_N}(x)$ has a Gaussian form if and only if all the random terms are…

Probability · Mathematics 2013-05-29 Michael I. Tribelsky

This article focuses on properties of monotone convolutions. A criterion for infinite divisibility and time evolution of convolution semigroups are mainly studied. In particular, we clarify that many analogues of the classical results of…

Operator Algebras · Mathematics 2010-08-30 Takahiro Hasebe

Deep generative models are routinely used in generating samples from complex, high-dimensional distributions. Despite their apparent successes, their statistical properties are not well understood. A common assumption is that with enough…

Machine Learning · Statistics 2025-01-15 Edric Tam , David B. Dunson