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A general lower bound is developed for the minimax risk when estimating an arbitrary functional. The bound is based on testing two composite hypotheses and is shown to be effective in estimating the nonsmooth functional…

Statistics Theory · Mathematics 2011-05-17 T. Tony Cai , Mark G. Low

Various key problems from theoretical computer science can be expressed as polynomial optimization problems over the boolean hypercube. One particularly successful way to prove complexity bounds for these types of problems are based on sums…

Data Structures and Algorithms · Computer Science 2018-02-28 Mareike Dressler , Adam Kurpisz , Timo de Wolff

We point out necessary and sufficient conditions of uniform consistency of nonparametric sets of alternatives for widespread nonparametric tests. Nonparametric sets of alternatives can be defined both in terms of distribution function and…

Statistics Theory · Mathematics 2020-09-01 Mikhail Ermakov

Geometry of sparse systems of polynomial equations (i.e. the ones with prescribed monomials and generic coefficients) is well studied in terms of their Newton polytopes. The results of this study are colloquially known as the…

Algebraic Geometry · Mathematics 2024-01-23 Alexander Esterov

In this work, we consider a multivariate regression model with one-sided errors. We assume for the regression function to lie in a general H\"{o}lder class and estimate it via a nonparametric local polynomial approach that consists of…

Statistics Theory · Mathematics 2021-02-11 Leonie Selk , Charles Tillier , Orlando Marigliano

We introduce a Bernstein-type inequality which serves to uniformly control quadratic forms of gaussian variables. The latter can for example be used to derive sharp model selection criteria for linear estimation in linear regression and…

Statistics Theory · Mathematics 2009-09-22 Ikhlef Bechar

Let F be a homogeneous real polynomial of even degree in any number of variables. We consider the problem of giving explicit conditions on the coefficients so that F is positive definite or positive semi-definite. In this note we produce a…

Algebraic Geometry · Mathematics 2007-05-23 Fernando Cukierman

We present a subdivision method to solve systems of congruence equations. This method is inspired in a subdivision method, based on Bernstein forms, to solve systems of polynomial inequalities in several variables and arbitrary degrees. The…

Optimization and Control · Mathematics 2017-08-08 César Massri , Manuel Dubinsky

Multivariate nonnegative orthant data are real vectors bounded to the left by the null vector, and they can be continuous, discrete or mixed. We first review the recent relative variability indexes for multivariate nonnegative continuous…

Methodology · Statistics 2021-09-08 Célestin C. Kokonendji , Sobom M. Somé

We review several (and provide new) results on the theory of moments, sums of squares and basic semi-algebraic sets when convexity is present. In particular, we show that under convexity, the hierarchy of semidefinite relaxations for…

Optimization and Control · Mathematics 2008-12-04 Jean B. Lasserre

We consider numerical certification of approximate solutions to a system of polynomial equations with more equations than unknowns by first certifying solutions to a square subsystem. We give several approaches that certifiably select which…

Algebraic Geometry · Mathematics 2020-07-07 Timothy Duff , Nickolas Hein , Frank Sottile

A non-negativity certificate (NNC) is a way to write a polynomial so that its non-negativity on a semialgebraic set becomes evident. Positivstellens\"atze (Ps\"atze) guarantee the existence of NNCs. Both, NNCs and Ps\"atze underlie powerful…

Optimization and Control · Mathematics 2024-03-25 Olga Kuryatnikova , Juan C. Vera , Luis F. Zuluaga

A barrier certificate often serves as an inductive invariant that isolates an unsafe region from the reachable set of states, and hence is widely used in proving safety of hybrid systems possibly over an infinite time horizon. We present a…

Logic in Computer Science · Computer Science 2022-09-21 Qiuye Wang , Mingshuai Chen , Bai Xue , Naijun Zhan , Joost-Pieter Katoen

Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong.…

Methodology · Statistics 2022-10-12 Jiří Dvořák , Tomáš Mrkvička

We study operator-norm covariance estimation from heavy-tailed samples that may include a small fraction of arbitrary outliers. A simple and widely used safeguard is \emph{Euclidean norm clipping}, but its accuracy depends critically on an…

Machine Learning · Statistics 2026-02-17 Even He , Zaizai Yan

In this article, we give an account of some recent irreducibility testing criteria for polynomials having integer coefficients over the field of rational numbers.

Number Theory · Mathematics 2023-10-05 Sanjeev Kumar , Jitender Singh

This paper proposes a new test for inequalities that are linear in possibly partially identified nuisance parameters. This type of hypothesis arises in a broad set of problems, including subvector inference for linear unconditional moment…

Methodology · Statistics 2025-11-06 Gregory Fletcher Cox , Xiaoxia Shi , Yuya Shimizu

B{\'e}zout 's theorem states that dense generic systems of n multivariate quadratic equations in n variables have 2 n solutions over algebraically closed fields. When only a small subset M of monomials appear in the equations (fewnomial…

Symbolic Computation · Computer Science 2016-08-22 Jean-Charles Faugere , Pierre-Jean Spaenlehauer , Jules Svartz

In this letter we consider the problem of certification of quantum measurements with an arbitrary number of outcomes. We propose a simple scheme for certifying any set of $d$-outcome projective measurements which do not share any common…

Quantum Physics · Physics 2022-10-26 Shubhayan Sarkar , Debashis Saha , Remigiusz Augusiak

Randomized smoothing has established state-of-the-art provable robustness against $\ell_2$ norm adversarial attacks with high probability. However, the introduced Gaussian data augmentation causes a severe decrease in natural accuracy. We…

Machine Learning · Computer Science 2020-12-01 Ching-Chia Kao , Jhe-Bang Ko , Chun-Shien Lu
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