English
Related papers

Related papers: Explicit error bounds for lattice Edgeworth expans…

200 papers

In this paper, we consider the sphericity test for a one-sample problem under high-dimensional two-step monotone incomplete data. Existing asymptotic expansions for the null distributions of the likelihood ratio test (LRT) statistic and…

Statistics Theory · Mathematics 2026-04-01 Tetsuya Sato , Tomoyuki Nakagawa

We give explicit bounds for the tail probabilities for sums of independent geometric or exponential variables, possibly with different parameters.

Probability · Mathematics 2017-09-26 Svante Janson

Lower and upper bounds are explored for the uniform (Kolmogorov) and $L^2$-distances between the distributions of weighted sums of dependent summands and the normal law. The results are illustrated for several classes of random variables…

Probability · Mathematics 2023-08-08 S. G. Bobkov , G. P. Chistyakov , F. Götze

We get the computable error bounds for generalized Cornish-Fisher expansions for quantiles of statistics provided that the computable error bounds for Edgeworth-Chebyshev type expansions for distributions of these statistics are known. The…

Statistics Theory · Mathematics 2017-03-06 V. V. Ulyanov , M. Aoshima , Y. Fujikoshi

Classical Edgeworth expansions provide asymptotic correction terms to the Central Limit Theorem (CLT) up to an order that depends on the number of moments available. In this paper, we provide subsequent correction terms beyond those given…

Probability · Mathematics 2011-03-23 Henry Lam , Jose Blanchet , Damian Burch , Martin Z. Bazant

This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling. We prove Edgeworth expansions for $t$ statistics and coverage error expansions for interval estimators that (i)…

Econometrics · Economics 2021-07-26 Sebastian Calonico , Matias D. Cattaneo , Max H. Farrell

We develop generalized approach to obtaining Edgeworth expansions for $t$-statistics of an arbitrary order using computer algebra and combinatorial algorithms. To incorporate various versions of mean-based statistics, we introduce Adjusted…

Statistics Theory · Mathematics 2021-05-18 Inna Gerlovina , Alan E. Hubbard

In this article, we study a class of lattice random variables in the domain of attraction of an $\alpha$-stable random variable with index $\alpha \in (0,2)$ which satisfy a truncated fractional Edgeworth expansion. Our results include…

Probability · Mathematics 2023-06-30 Leandro Chiarini , Milton Jara , Wioletta M. Ruszel

An upper bound on the error probability of specific lattices, based on their distance-spectrum, is constructed. The derivation is accomplished using a simple alternative to the Minkowski-Hlawka mean-value theorem of the geometry of numbers.…

Information Theory · Computer Science 2013-01-03 Yuval Domb , Meir Feder

In this paper, we present explicit and computable error bounds for the asymptotic expansions of the Hermite polynomials with Plancherel--Rotach scale. Three cases, depending on whether the scaled variable lies in the outer or oscillatory…

Classical Analysis and ODEs · Mathematics 2021-11-16 Wei Shi , Gergő Nemes , Xiang-Sheng Wang , Roderick Wong

We establish higher-order nonasymptotic expansions for a difference between probability distributions of sums of i.i.d. random vectors in a Euclidean space. The derived bounds are uniform over two classes of sets: the set of all Euclidean…

Statistics Theory · Mathematics 2022-11-30 Mayya Zhilova

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 prove a sharp bound for the remainder term of the number of lattice points inside a ball, when averaging over a compact set of (not necessarily unimodular) lattices, in dimensions two and three. We also prove that such a bound cannot…

Number Theory · Mathematics 2013-11-13 Samuel Holmin

Recently, the present authors derived new asymptotic expansions for linear differential equations having a simple turning point. These involve Airy functions and slowly varying coefficient functions, and were simpler than previous…

Classical Analysis and ODEs · Mathematics 2020-04-28 T. M. Dunster , A. Gil , J. Segura

In complex Langevin simulations, the insufficient decay of the probability density near infinity leads to boundary terms that spoil the formal argument for correctness. We present a formulation of this term that is cheaply measurable in…

High Energy Physics - Lattice · Physics 2021-12-07 Michael W. Hansen , Erhard Seiler , Dénes Sexty , Ion-Olimipu Stamatescu

We construct a new tail bound for the sum of independent random variables for situations in which the expected value of the sum is known and each random variable lies within a specified interval, which may be different for each variable.…

Probability · Mathematics 2025-03-25 Jackson Loper , Jeffrey Regier

Consider uniformly elliptic random walk on $\bbZ^d$ with independent jump rates across nearest neighbour bonds of the lattice. We show that the infinite volume effective diffusion matrix can be almost surely recovered as the limit of finite…

Probability · Mathematics 2015-06-26 Pietro Caputo , Dima Ioffe

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

In this paper, we study the risk bounds for samples independently drawn from an infinitely divisible (ID) distribution. In particular, based on a martingale method, we develop two deviation inequalities for a sequence of random variables of…

Machine Learning · Statistics 2012-02-20 Chao Zhang , Dacheng Tao

In this work, we derive an Edgeworth expansion for the Bernoulli weighted mean $\hat{\mu} = \frac{\sum_{i=1}^n Y_i T_i}{\sum_{i=1}^n T_i}$ in the case where $Y_1, \dots, Y_n$ are i.i.d. non semi-lattice random variables and $T_1, \dots,…

Probability · Mathematics 2022-08-22 Pierre-Louis Cauvin