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If a functional in an inverse problem can be estimated with parametric rate, then the minimax rate gives no information about the ill-posedness of the problem. To have a more precise lower bound, we study semiparametric efficiency in the…

Statistics Theory · Mathematics 2014-05-07 Mathias Trabs

We consider exponential functionals of a multi-dimensional Brownian motion with drift, defined via a collection of linear functionals. We give a characterization of the Laplace transform of their joint law as the unique bounded solution, up…

Probability · Mathematics 2026-01-13 Fabrice Baudoin , Neil O'Connell

A real valued function $f$ defined on a real open interval $I$ is called $\Phi$-convex if, for all $x,y\in I$, $t\in[0,1]$ it satisfies $$ f(tx+(1-t)y)\leq tf(x)+(1-t)f(y)+t\Phi\big((1-t)|x-y|\big)+(1-t)\Phi\big(t|x-y|\big), $$ where…

Classical Analysis and ODEs · Mathematics 2020-12-23 Angshuman R. Goswami , Zsolt Páles

We develop nonparametric regression methods for the case when the true regression function is not necessarily smooth. More specifically, our approach is using the fractional Laplacian and is designed to handle the case when the true…

Statistics Theory · Mathematics 2025-06-11 Zhaoyang Shi , Krishnakumar Balasubramanian , Wolfgang Polonik

We consider work fluctuation relations (FRs) for generic types of dynamics generating anomalous diffusion: Levy flights, long-correlated Gaussian processes and time-fractional kinetics. By combining Langevin and kinetic approaches we…

Statistical Mechanics · Physics 2009-03-24 A. V. Chechkin , R. Klages

Conservation laws are usually studied in the context of sufficient regularity conditions imposed on the flux function, usually $C^{2}$ and uniform convexity. Some results are proven with the aid of variational methods and a unique minimizer…

Analysis of PDEs · Mathematics 2018-03-06 Carey Caginalp

We show weak lower semi-continuity of functionals assuming the new notion of a "convexly constrained" $\mathcal A$-quasiconvex integrand. We assume $\mathcal A$-quasiconvexity only for functions defined on a set $K$ which is convex.…

Analysis of PDEs · Mathematics 2021-02-01 Jack W. D. Skipper , Emil Wiedemann

In this paper, we consider the sublinear expectation on bounded random variables. With the notion of uncorrelatedness for random variables under the sublinear expectation, a weak law of large numbers is obtained. With the notion of…

Probability · Mathematics 2023-11-17 Wenhao Li , Chuanfeng Sun

We consider nonconvex real valued functions whose truncations are either quasiconvex or even convex starting with a certain level. Among them, the $C^2$-smooth functions whose level sets are all completely contained in the positive definite…

Classical Analysis and ODEs · Mathematics 2026-03-05 Cornel Pintea

The ultimate goal of regression analysis is to obtain information about the conditional distribution of a response given a set of explanatory variables. This goal is, however, seldom achieved because most established regression models only…

Methodology · Statistics 2017-12-13 Torsten Hothorn , Thomas Kneib , Peter Bühlmann

This paper presents a general study of one-dimensional differentiability for functionals defined on convex domains that are not necessarily open. The local approximation is carried out using affine functionals, as opposed to linear…

Functional Analysis · Mathematics 2025-07-04 Simone Cerreia-Vioglio , Fabio Maccheroni , Massimo Marinacci , Luigi Montrucchio , Lorenzo Stanca

We study estimation of a multivariate function $f:\mathbf{R}^d\to\mathbf{R}$ when the observations are available from the function $Af$, where $A$ is a known linear operator. Both the Gaussian white noise model and density estimation are…

Statistics Theory · Mathematics 2010-01-14 Jussi Klemelä , Enno Mammen

Take a random variable X with some finite exponential moments. Define an exponentially weighted expectation by E^t(f) = E(e^{tX}f)/E(e^{tX}) for admissible values of the parameter t. Denote the weighted expectation of X itself by r(t) =…

Probability · Mathematics 2007-11-07 Marton Balazs , Timo Seppalainen

Suppose $\{X_{t}:t\ge 0\}$ is a supercritical superprocess on a Luzin space $E$, with a non-local branching mechanism and probabilities $\mathbb{P}_{\delta_{x}}$, when initiated from a unit mass at $x\in E$. By ``supercritical", we mean…

Probability · Mathematics 2025-09-11 Ting Yang

In this paper we provide a theoretical analysis of counterfactual invariance. We present a variety of existing definitions, study how they relate to each other and what their graphical implications are. We then turn to the current major…

Machine Learning · Computer Science 2023-07-18 Jake Fawkes , Robin J. Evans

A geometric setup for constrained variational calculus is presented. The analysis deals with the study of the extremals of an action functional defined on piecewise differentiable curves, subject to differentiable, non-holonomic…

Mathematical Physics · Physics 2015-05-08 Enrico Massa , Danilo Bruno , Gianvittorio Luria , Enrico Pagani

We study the approximation of functions which are invariant with respect to certain permutations of the input indices using flow maps of dynamical systems. Such invariant functions includes the much studied translation-invariant ones…

Machine Learning · Computer Science 2022-08-19 Qianxiao Li , Ting Lin , Zuowei Shen

The Bank-Laine conjecture concerning the oscillation of solutions of second order homogeneous linear differential equations has recently been disproved by Bergweiler and Eremenko. It is shown here, however, that the conjecture is true if…

Complex Variables · Mathematics 2018-12-24 J. K. Langley

Zhang's reverse affine isoperimetric inequality states that among all convex bodies $K\subseteq\mathbb{R}^n$, the affine invariant quantity $|K|^{n-1}|\Pi^*(K)|$ (where $\Pi^*(K)$ denotes the polar projection body of $K$) is minimized if…

Functional Analysis · Mathematics 2018-10-18 David Alonso-Gutiérrez , Julio Bernués , Bernardo González Merino

We consider inference for the mean and covariance functions of covariate adjusted functional data using Local Linear Kernel (LLK) estimators. By means of a double asymptotic, we differentiate between sparse and dense covariate adjusted…

Methodology · Statistics 2018-02-28 Dominik Liebl