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We study asymptotic expansions in free probability. In a class of classical limit theorems Edgeworth expansion can be obtained via a general approach using sequences of "influence" functions of individual random elements described by…

Probability · Mathematics 2015-02-05 F. Götze , A. Reshetenko

We estimate the lattice sums arising in the context of the integer point counting in polyhedra.

Combinatorics · Mathematics 2026-05-14 M. M. Skriganov

The random vector of frequencies in a generalized urn model is viewed as conditionally independent random variables, given their sum. Such a representation is exploited to derive Edgeworth expansions for a sum of functions of such…

Probability · Mathematics 2014-01-20 Sh. M. Mirakhmedov , S. Rao Jammalamadaka , Ibrahim B. Mohamed

The purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails. In such situations, usual empirical averages may fail to provide reliable…

Methodology · Statistics 2016-08-11 Christian Brownlees , Emilien Joly , Gábor Lugosi

Computable and sharp error bounds are derived for asymptotic expansions for linear differential equations having a simple turning point. The expansions involve Airy functions and slowly varying coefficient functions. The sharpness of the…

Classical Analysis and ODEs · Mathematics 2020-09-11 T. M. Dunster , A. Gil , J. Segura

Edgeworth expansions of first and second order are established for general linear rank statistics under the null hypothesis with asymptotically ''sufficiently'' small remainder terms. The methods used are the Stein method combined with an…

Statistics Theory · Mathematics 2025-11-18 Walter Schneller

In this paper we present a new perspective on error analysis of Legendre approximations for differentiable functions. We start by introducing a sequence of Legendre-Gauss-Lobatto polynomials and prove their theoretical properties, such as…

Numerical Analysis · Mathematics 2023-12-15 Haiyong Wang

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

We develop a higher-order asymptotic analysis for the semi-hard triplet loss using the Edgeworth expansion. It is known that this loss function enforces that embeddings of similar samples are close while those of dissimilar samples are…

Machine Learning · Statistics 2025-03-18 Masanari Kimura

A new approach of implementing initial and boundary conditions for the lattice Boltzmann method is presented. The new approach is based on an extended collision operator that uses the gradients of the fluid velocity. The numerical…

comp-gas · Physics 2009-10-22 P. A. Skordos

We establish the validity of the empirical Edgeworth expansion (EE) for a studentized trimmed mean, under the sole condition that the underlying distribution function of the observations satisfies a local smoothness condition near the two…

Statistics Theory · Mathematics 2011-06-28 Nadezhda Gribkova , Roelof Helmers

We study higher order expansions both in the Berry-Ess\'een estimate (Edgeworth expansions) and in the local limit theorems for Birkhoff sums of chaotic probability preserving dynamical systems. We establish general results under technical…

Dynamical Systems · Mathematics 2021-11-15 Kasun Fernando , Françoise Pène

We introduce a modified version of the excess risk, which can be used to obtain tighter, fast-rate PAC-Bayesian generalisation bounds. This modified excess risk leverages information about the relative hardness of data examples to reduce…

Machine Learning · Computer Science 2023-01-25 Felix Biggs , Benjamin Guedj

Finite-width fully connected neural networks with Gaussian-initialized weights deviate from their infinite-width Gaussian limit, exhibiting non-vanishing higher-order cumulants. We approximate these deviations, for a neural network…

Machine Learning · Statistics 2026-05-26 Lucia Celli

Stochastic iterative methods are useful in a variety of large-scale numerical linear algebraic, machine learning, and statistical problems, in part due to their low-memory footprint. They are frequently used in a variety of applications,…

Numerical Analysis · Mathematics 2025-11-27 Toby Anderson , Max Collins , Jamie Haddock , Jackie Lok , Elizaveta Rebrova

In this article, we derive an explicit formula for computing confidence interval for the mean of a bounded random variable. Moreover, we have developed multistage point estimation methods for estimating the mean value with prescribed…

Statistics Theory · Mathematics 2010-11-29 Xinjia Chen

The work of this paper is devoted to obtaining strong laws for intermediately trimmed sums of random variables with infinite means. Particularly, we provide conditions under which the intermediately trimmed sums of independent but not…

Probability · Mathematics 2023-10-03 Rita Giuliano , Milto Hadjikyriakou

We compute the first three terms of the 1/d expansions for the growth constants and one-point functions of nearest-neighbour lattice trees and lattice (bond) animals on the integer lattice Zd, with rigorous error estimates. The proof uses…

Probability · Mathematics 2015-03-20 Yuri Mejia Miranda , Gordon Slade

Known Bernstein-type upper bounds on the tail probabilities for sums of independent zero-mean sub-exponential random variables are improved in several ways at once. The new upper bounds have a certain optimality property.

Probability · Mathematics 2022-08-15 Iosif Pinelis

The following problem is considered: given a joint distribution $P_{XY}$ and an event $E$, bound $P_{XY}(E)$ in terms of $P_XP_Y(E)$ (where $P_XP_Y$ is the product of the marginals of $P_{XY}$) and a measure of dependence of $X$ and $Y$.…

Information Theory · Computer Science 2019-03-12 Ibrahim Issa , Amedeo Roberto Esposito , Michael Gastpar