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We provide a simple proof that the partial sums $\sum_{n\leq x}f(n)$ of a Rademacher random multiplicative function $f$ change sign infinitely often as $x\to\infty$, almost surely.

Number Theory · Mathematics 2022-05-31 Marco Aymone , Winston Heap , Jing Zhao

We consider the equation R(n)=Q(n)+M(n) R(n-1), with random non-i.i.d. coefficients (Q(n),M(n)), and show that the distribution tails of the stationary solution to this equation are regularly varying at infinity.

Probability · Mathematics 2010-06-15 A. P. Ghosh , D. Hay , V. Hirpara , R. Rastegar , A. Roitershtein , A. Schulteis , J. Suh

We consider moderately trimmed sums of non-negative i.i.d. random variables. We show that for every distribution function there exists a proper moderate trimming such that for the trimmed sum a non-trivial strong law of large numbers holds.…

Probability · Mathematics 2019-05-23 Marc Kesseböhmer , Tanja Schindler

Let $\{X_i\}_{i\geq1}$ be an i.i.d. sequence of random variables and define, for $n\geq2$, \[T_n=\cases{n^{-1/2}\hat{\sigma}_n^{-1}S_n,\quad \hat{\sigma}_n>0,\cr 0,\quad \hat{\sigma}_n=0,}with S_n=\sum_{i=1}^nX_i,…

Statistics Theory · Mathematics 2011-02-11 Fredrik Jonsson

We prove tail estimates for variables $\sum_i f(X_i)$, where $(X_i)_i$ is the trajectory of a random walk on an undirected graph (or, equivalently, a reversible Markov chain). The estimates are in terms of the maximum of the function $f$,…

Probability · Mathematics 2007-12-25 Roy Wagner

We present a method for upper and lower bounding the right and the left tail probabilities of continuous random variables (RVs). For the right tail probability of RV $X$ with probability density function $f (x)$, this method requires first…

Probability · Mathematics 2026-01-07 Nikola Zlatanov

We present upper and lower bounds for the tail distribution of the stationary waiting time $D$ in the stable $GI/GI/s$ FCFS queue. These bounds depend on the value of the traffic load $\rho$ which is the ratio of mean service and mean…

Probability · Mathematics 2013-03-20 Sergey Foss , Dmitry Korshunov

We survey known solutions to the infinite extendibility problem for (necessarily exchangeable) probability laws on $\mathbb{R}^d$, which is: Can a given random vector $\vec{X} = (X_1,\ldots,X_d)$ be represented in distribution as the first…

Probability · Mathematics 2020-11-06 Jan-Frederik Mai

Consider $n$ real/complex, independent/dependent random variables with respective tail bounds and $g$ a measurable function of the r.v.'s. Consider $f$ the "sharpest" tail bound of $g$ (sharpest in the sense that if $f$ were any less, then…

Probability · Mathematics 2026-05-26 Stephen Jordan Harrison

Regular variation is often used as the starting point for modeling multivariate heavy-tailed data. A random vector is regularly varying if and only if its radial part $R$ is regularly varying and is asymptotically independent of the angular…

Statistics Theory · Mathematics 2018-03-28 Phyllis Wan , Richard A. Davis

Stretched exponential probability density functions (pdf), having the form of the exponential of minus a fractional power of the argument, are commonly found in turbulence and other areas. They can arise because of an underlying random…

Statistical Mechanics · Physics 2009-10-30 U. Frisch , D. Sornette

Multivariate regular variation plays a role assessing tail risk in diverse applications such as finance, telecommunications, insurance and environmental science. The classical theory, being based on an asymptotic model, sometimes leads to…

Probability · Mathematics 2011-08-31 Bikramjit Das , Abhimanyu Mitra , Sidney Resnick

It is shown phenomenologically that the fractional derivative $\xi=D^\alpha u$ of order $\alpha$ of a multifractal function has a power-law tail $\propto |\xi| ^{-p_\star}$ in its cumulative probability, for a suitable range of $\alpha$'s.…

Chaotic Dynamics · Physics 2015-06-26 U. Frisch , T. Matsumoto

Convolutions of long-tailed and subexponential distributions play a major role in the analysis of many stochastic systems. We study these convolutions, proving some important new results through a simple and coherent approach, and showing…

Probability · Mathematics 2017-11-29 Sergey Foss , Dmitry Korshunov , Stan Zachary

A rapid transformation is derived between spherical harmonic expansions and their analogues in a bivariate Fourier series. The change of basis is described in two steps: firstly, expansions in normalized associated Legendre functions of all…

Numerical Analysis · Mathematics 2017-11-07 Richard Mikael Slevinsky

We study large deviation probabilities for a sum of dependent random variables from a heavy-tailed factor model, assuming that the components are regularly varying. We identify conditions where both the factor and the idiosyncratic terms…

Probability · Mathematics 2007-12-05 Boualem Djehiche , Jens Svensson

Using a multivariable Faa di Bruno formula we give conditions on transformations $\tau:[0,1]^m\to\mathcal{X}$ where $\mathcal{X}$ is a closed and bounded subset of $\mathbb{R}^d$ such that $f\circ\tau$ is of bounded variation in the sense…

Computation · Statistics 2015-12-10 Kinjal Basu , Art B. Owen

Let $\{X_1, X_2, ... \}$ be a sequence of dependent heavy-tailed random variables with distributions $F_1, F_2,...$ on $(-\infty,\infty)$, and let $\tau$ be a nonnegative integer-valued random variable independent of the sequence $\{X_k, k…

Probability · Mathematics 2013-02-28 Kam Chuen Yuen , Chuancun Yin

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

Assessing the probability of occurrence of extreme events is a crucial issue in various fields like finance, insurance, telecommunication or environmental sciences. In a multivariate framework, the tail dependence is characterized by the…

Statistics Theory · Mathematics 2015-05-26 Nicolas Goix , Anne Sabourin , Stéphan Clémençon