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Let $\mu$ be a probability measure (or corresponding random variable) such that all moments $\mu_n$ exist. Knowledge of the moments is not sufficient to determine infinite divisibility of the measure; we show also that infinitely divisible,…

Probability · Mathematics 2007-05-23 Aubrey Wulfsohn

This paper develops a two-stage method for inference on partially identified parameters in moment inequality models with separable nuisance parameters. In the first stage, the nuisance parameters are estimated separately, and in the second…

Econometrics · Economics 2025-08-28 Xunkang Tian

A crucial assumption to reduce computational complexity in spatial-temporal data analysis is separability, which factors the covariance structure into a purely spatial and a purely temporal component. In this paper, we develop statistical…

Statistics Theory · Mathematics 2026-03-30 Lujia Bai , Holger Dette , Zihao Yuan

We consider a two-stage distributionally robust optimization (DRO) model with multimodal uncertainty, where both the mode probabilities and uncertainty distributions could be affected by the first-stage decisions. To address this setting,…

Optimization and Control · Mathematics 2026-02-03 Xian Yu , Beste Basciftci

In this paper, we introduce a general model for jointly modelling the nodal heterogeneity and covariates in weighted or unweighted bipartite networks, which contains two different types of nodes. The model has a degree heterogeneity…

Statistics Theory · Mathematics 2026-04-01 Wu Zuhui , Wang Qiuping , Yan Ting

Beginning with work of Zeilberger on classical pattern counts, there are a variety of structural results for moments of permutation statistics applied to random permutations. Using tools from representation theory, Gaetz and Ryba…

Combinatorics · Mathematics 2025-03-25 Zachary Hamaker , Brendon Rhoades

Despite the relevance of the binomial distribution for probability theory and applied statistical inference, its higher-order moments are poorly understood. The existing formulas are either not general enough, or not structured and…

Statistics Theory · Mathematics 2022-06-07 Maciej Skorski

We conduct a study based on the Bures (minimal monotone) metric, analogous to that recently reported for the Hilbert-Schmidt (flat or Euclidean) metric (arXiv:0704.3723v2). Among the interesting results obtained there had been…

Mathematical Physics · Physics 2007-08-31 Paul B. Slater

We consider Gibbs distributions, which are families of probability distributions over a discrete space $\Omega$ with probability mass function of the form $\mu^\Omega_\beta(\omega) \propto e^{\beta H(\omega)}$ for $\beta$ in an interval…

Data Structures and Algorithms · Computer Science 2025-04-04 David G. Harris , Vladimir Kolmogorov

Distances between probability distributions that take into account the geometry of their sample space,like the Wasserstein or the Maximum Mean Discrepancy (MMD) distances have received a lot of attention in machine learning as they can, for…

Machine Learning · Computer Science 2020-04-29 Gaëtan Hadjeres , Frank Nielsen

The author uses a Stein-type covariance identity to obtain moment estimators for the parameters of the quadratic polynomial subfamily of Pearson distributions. The asymptotic distribution of the estimators is obtained, and normality and…

Statistics Theory · Mathematics 2018-06-08 Giorgos Afendras

We study a variant of quantum hypothesis testing wherein an additional 'inconclusive' measurement outcome is added, allowing one to abstain from attempting to discriminate the hypotheses. The error probabilities are then conditioned on a…

Quantum Physics · Physics 2024-07-09 Bartosz Regula , Ludovico Lami , Mark M. Wilde

We derive a moment formula for generalized fractional polynomial processes, i.e., for polynomial-preserving Markov processes time-changed by an inverse L\'evy-subordinator. If the time change is inverse $\alpha$-stable, the time-derivative…

Probability · Mathematics 2026-02-27 Johannes Assefa , Martin Keller-Ressel

Recent results in quantization theory show that the mean-squared expected distortion can reach a rate of convergence of $\mathcal{O}(1/n)$, where $n$ is the sample size [see, e.g., IEEE Trans. Inform. Theory 60 (2014) 7279-7292 or Electron.…

Statistics Theory · Mathematics 2015-04-02 Clément Levrard

Random matrix theory (RMT) universality is the defining property of quantum mechanical chaotic systems, and can be probed by observables like the spectral form factor (SFF). In this paper, we describe systematic deviations from RMT…

Statistical Mechanics · Physics 2025-01-15 Rahel L. Baumgartner , Luca V. Delacrétaz , Pranjal Nayak , Julian Sonner

We present estimators for smooth Hilbert-valued parameters, where smoothness is characterized by a pathwise differentiability condition. When the parameter space is a reproducing kernel Hilbert space, we provide a means to obtain efficient,…

Statistics Theory · Mathematics 2023-09-28 Alex Luedtke , Incheoul Chung

The (complex) two-qubit systems comprise a 15-dimensional convex set and the real two-qubit systems, a 9-dimensional convex set. While formulas for the Hilbert-Schmidt volumes of these two sets are known -- owing to recent important work of…

Quantum Physics · Physics 2007-05-23 Paul B. Slater

In the presence of Lindblad decoherence, i.e. dissipative effects in an open quantum system due to interaction with an environment, we examine the transition probabilities between the eigenstates in the two-level quantum system described by…

Quantum Physics · Physics 2024-04-24 Tommy Ohlsson , Shun Zhou

We study regularity properties for invariant measures of semilinear diffusions in a separable Hilbert space. Based on a pathwise estimate for the underlying stochastic convolution, we prove a priori estimates on such invariant measures. As…

Probability · Mathematics 2022-11-15 Abdelhadi Es-Sarhir , Wilhelm Stannat

The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples…

Machine Learning · Computer Science 2017-09-05 A. N. Gorban , I. Y. Tyukin
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