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The main results in this paper concern large deviations for families of non-Gaussian processes obtained as suitable perturbations of continuous centered multivariate Gaussian processes which satisfy a large deviation principle. We present…

Probability · Mathematics 2023-07-06 C. Macci , B. Pacchiarotti

This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\ell_1$-regularized maximum-likelihood estimation, which can be…

Machine Learning · Computer Science 2012-10-01 Xiao-Tong Yuan , Tong Zhang

An introduction to the methods and ideas of Chiral Perturbation Theory is presented in this talk. The discussion is illustrated with some phenomenological predictions that can be compared with available experimental results.

High Energy Physics - Phenomenology · Physics 2015-06-25 F. Cornet

Using a family of modified Weibull distributions, encompassing both sub-exponentials and super-exponentials, to parameterize the marginal distributions of asset returns and their multivariate generalizations with Gaussian copulas, we offer…

Physics and Society · Physics 2009-11-10 Y. Malevergne , D. Sornette

The complex Gaussian distribution has been widely used as a fundamental spectral and noise model in signal processing and communication. However, its Gaussian structure often limits its ability to represent the diverse amplitude…

Machine Learning · Statistics 2026-03-30 Toru Nakashika

We consider the estimation of the covariance of a stationary Gaussian process on a multi-dimensional grid from observations taken on a general acquisition domain. We derive spectral-norm risk rates for multi-taper estimators. When applied…

Statistics Theory · Mathematics 2022-05-16 José Luis Romero , Michael Speckbacher

Given an unconditional diffusion model targeting a joint model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the…

Machine Learning · Statistics 2025-02-21 Adrien Corenflos , Zheng Zhao , Simo Särkkä , Jens Sjölund , Thomas B. Schön

Weak convergence of joint distributions generally does not imply convergence of conditional distributions. In particular, conditional distributions need not converge when joint Gaussian distributions converge to a singular Gaussian limit.…

Statistics Theory · Mathematics 2025-10-15 Sarah Lumpp , Mathias Drton

Partially quenched theories are theories in which the valence- and sea-quark masses are different. Here, we will discuss the nonanalytic one-loop corrections to some physical quantities, using partially quenched chiral perturbation theory.…

High Energy Physics - Lattice · Physics 2009-10-31 Maarten Golterman , Ka Chun Leung

Calculation of the distribution of the average value of a Gaussian random field in a finite domain is carried out for different cases. The results of the calculation demonstrate a strong dependence of the width of the distribution on the…

Disordered Systems and Neural Networks · Physics 2015-05-13 S. V. Novikov , M. Van der Auweraer

Gaussian couplings of partial sum processes are derived for the high-dimensional regime $d=o(n^{1/3})$. The coupling is derived for sums of independent random vectors and subsequently extended to nonstationary time series. Our inequalities…

Probability · Mathematics 2022-03-08 Fabian Mies , Ansgar Steland

Comparing counterfactual distributions can provide more nuanced and valuable measures for causal effects, going beyond typical summary statistics such as averages. In this work, we consider characterizing causal effects via distributional…

Machine Learning · Statistics 2024-11-05 Kwangho Kim , Jisu Kim , Edward H. Kennedy

The PAC-Bayesian approach is a powerful set of techniques to derive non- asymptotic risk bounds for random estimators. The corresponding optimal distribution of estimators, usually called the Gibbs posterior, is unfortunately intractable.…

Machine Learning · Statistics 2015-06-16 Pierre Alquier , James Ridgway , Nicolas Chopin

Models for extreme values are generally derived from limit results, which are meant to be good enough approximations when applied to finite samples. Depending on the speed of convergence of the process underlying the data, these…

Statistics Theory · Mathematics 2019-02-20 Thomas Lugrin , Anthony C. Davison , Jonathan A. Tawn

We study three quasicontinuum approximations of a lattice model for crack propagation. The influence of the approximation on the bifurcation patterns is investigated. The estimate of the modeling error is applicable to near and beyond…

Numerical Analysis · Mathematics 2013-10-03 Xiantao Li , Pingbing Ming

Using a semi-parametric approach based on the fourth-order Edgeworth expansion for the unknown signal distribution, we derive an explicit expression for the likelihood detection statistic in the presence of non-normally distributed…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-19 Lionel Martellini , Tania Regimbau

Small sample sizes in clinical studies arises from factors such as reduced costs, limited subject availability, and the rarity of studied conditions. This creates challenges for accurately calculating confidence intervals (CIs) using the…

Methodology · Statistics 2025-11-11 Mulan Wu , Mengyu Xu , Dongyun Kim

We give a brief introduction to chiral perturbation theory in its various settings. We discuss some applications of recent interest including chiral extrapolations for lattice gauge theory.

High Energy Physics - Phenomenology · Physics 2008-11-26 Véronique Bernard , Ulf-G. Meißner

In this paper, we focus on multivariate doubly truncated first two moments of generalized skew-elliptical (GSE) distributions and derive explicit expressions for them. It includes many useful distributions, for examples, generalized…

Statistics Theory · Mathematics 2022-03-03 Baishuai Zuo , Chuancun Yin

Filtering problems with general exponential quadratic criteria are investigated for Gauss-Markov processes. In this setting, the Linear Exponential Gaussian and Risk-Sensitive filtering problems are solved and it is shown that they may have…

Probability · Mathematics 2009-02-06 M. L. Keptsyna , A. Le Breton , M. Viot