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Consider a branching random walk on $\mathbb Z$ in discrete time. Denote by $L_n(k)$ the number of particles at site $k\in\mathbb Z$ at time $n\in\mathbb N_0$. By the profile of the branching random walk (at time $n$) we mean the function…

Probability · Mathematics 2016-06-14 Rudolf Grübel , Zakhar Kabluchko

We prove an asymptotic Edgeworth expansion for the profiles of certain random trees including binary search trees, random recursive trees and plane-oriented random trees, as the size of the tree goes to infinity. All these models can be…

Probability · Mathematics 2017-10-06 Zakhar Kabluchko , Alexander Marynych , Henning Sulzbach

Edgeworth-type expansions for convolutions of probability densities and powers of the characteristic functions with non-uniform error terms are established for i.i.d. random variables with finite (fractional) moments of order $s \geq 2$,…

Probability · Mathematics 2011-04-20 S. G. Bobkov , G. P. Chistyakov , F. Götze

We discuss sufficient conditions that guarantee the existence of asymptotic expansions for the Central Limit Theorem for weakly dependent random variables including observations arising from sufficiently chaotic dynamical systems like…

Probability · Mathematics 2021-04-06 Kasun Fernando , Carlangelo Liverani

We consider a discrete-time branching random walk in the boundary case, where the associated random walk is in the domain of attraction of an $\alpha$-stable law with $1<\alpha<2$. We prove that the derivative martingale $D_n$ converges to…

Probability · Mathematics 2016-10-13 Hui He , Jingning Liu , Mei Zhang

Edgeworth expansions for random walks on covering graphs with groups of polynomial volume growths are obtained under a few natural assumptions. The coefficients appearing in this expansion depends on not only geometric features of the…

Probability · Mathematics 2023-06-05 Ryuya Namba

This paper presents new results on the Edgeworth expansion for high frequency functionals of continuous diffusion processes. We derive asymptotic expansions for weighted functionals of the Brownian motion and apply them to provide the…

Probability · Mathematics 2013-09-10 Mark Podolskij , Nakahiro Yoshida

An Edgeworth-type expansion is established for the entropy distance to the class of normal distributions of sums of i.i.d. random variables or vectors, satisfying minimal moment conditions.

Probability · Mathematics 2013-07-25 Sergey G. Bobkov , Gennadiy P. Chistyakov , Friedrich Götze

The problem of sums of independent, identically distributed random variables with stretched-exponential tails exhibits a dynamical phase transition and has recently reemerged in the context of active transport and condensation phenomena. We…

Statistical Mechanics · Physics 2026-05-11 Alberto Bassanoni , Omer Hamdi

Consider a branching random walk in which the offspring distribution and the moving law both depend on an independent and identically distributed random environment indexed by the time.For the normalised counting measure of the number of…

Probability · Mathematics 2016-11-01 Zhi-Qiang Gao , Quansheng Liu

We establish a variety of properties of the discrete time simple random walk on a Galton-Watson tree conditioned to survive when the offspring distribution, $Z$ say, is in the domain of attraction of a stable law with index…

Probability · Mathematics 2012-10-24 David A. Croydon , Takashi Kumagai

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

The "typical" asymptotic behavior of the weighted sums of independent, identically distibuted random vectors in k-dimensional space is considered. It is shown that under finitnes of fifth absolute moment of an individual term the rate of…

Probability · Mathematics 2023-12-25 Sagak Ayvazyan

In this paper, we derive valid Edgeworth expansions for studentized versions of a large class of statistics when the data are generated by a strongly mixing process. Under dependence, the asymptotic variance of such a statistic is given by…

Statistics Theory · Mathematics 2010-01-13 S. N. Lahiri

In this article we generalize the classical Edgeworth expansion for the probability density function (PDF) of sums of a finite number of symmetric independent identically distributed random variables with a finite variance to sums of…

Statistical Mechanics · Physics 2015-05-20 Netanel Hazut , Shlomi Medalion , David A. Kessler , Eli Barkai

For a branching random walk that drifts to infinity, consider its Malthusian martingale, i.e.~the additive martingale with parameter $\theta$ being the smallest root of the characteristic equation. When particles are killed below the…

Probability · Mathematics 2025-05-20 Heng Ma , Pascal Maillard

For sequences of non-lattice weakly dependent random variables, we obtain asymptotic expansions for Large Deviation Principles. These expansions, commonly referred to as strong large deviation results, are in the spirit of Edgeworth…

Probability · Mathematics 2020-03-10 Kasun Fernando , Pratima Hebbar

We obtain large deviations for a class of dependent random variables in the domain of attraction of an $\alpha$-stable law, $\alpha\in (0, 1)\cup (1, 2]$. This class includes ergodic sums of observables in the domain of attraction of an…

Probability · Mathematics 2024-06-18 Jonny Imbierski , Dalia Terhesiu

We introduce a large and flexible class of discrete tempered stable distributions, and analyze the domains of attraction for both this class and the related class of positive tempered stable distributions. Our results suggest that these are…

Probability · Mathematics 2020-01-22 Michael Grabchak

We study the distribution of a general class of asymptoticallylinear statistics which are symmetric functions of $N$ independent observations. The distribution functions of these statistics are approximated by an Edgeworth expansion with a…

Statistics Theory · Mathematics 2021-02-09 Friedrich Götze , Mindaugas Bloznelis
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