Results 71 to 80 of about 33,958 (245)

Change Point Analysis for Functional Data Using Empirical Characteristic Functionals

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We develop a new method to detect change points in the distribution of functional data based on integrated CUSUM processes of empirical characteristic functionals. Asymptotic results are presented under conditions allowing for low‐order moments and serial dependence in the data establishing the limiting null‐distribution of the proposed test ...
Lajos Horváth   +2 more
wiley   +1 more source

Measure‐valued processes for energy markets

open access: yesMathematical Finance, Volume 35, Issue 2, Page 520-566, April 2025.
Abstract We introduce a framework that allows to employ (non‐negative) measure‐valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath–Jarrow–Morton approach can be translated to this framework, thus guaranteeing arbitrage free ...
Christa Cuchiero   +3 more
wiley   +1 more source

Poisson summation formula in weighted Lebesgue spaces

open access: yes
We characterize the parameters $(α,β,p,q)$ for which the condition $f|x|^α\in L^p$ and $\widehat{f}|ξ|^β\in L^q$ implies the validity of the Poisson summation formula, thus completing the study of Kahane and Lemarié-Rieusset.
Saucedo, Miquel, Tikhonov, Sergey
openaire   +2 more sources

Approximation theorems in weighted Lebesgue spaces with variable exponent

open access: yesFilomat, 2021
In this work, approximation properties of de la Vall?e-Poussin means are investigated in weighted Lebesgue spaces with variable exponent where weight function belongs to Muckenhoupt class. For this purpose direct, inverse and simultaneous theorems of approximation theory are proved and constructive characterizations of functions are ...
openaire   +4 more sources

Spatial depth for data in metric spaces

open access: yesScandinavian Journal of Statistics, EarlyView.
Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley   +1 more source

Isoperimetric inequalities on slabs with applications to cubes and Gaussian slabs

open access: yesCommunications on Pure and Applied Mathematics, Volume 79, Issue 4, Page 1012-1072, April 2026.
Abstract We study isoperimetric inequalities on “slabs”, namely weighted Riemannian manifolds obtained as the product of the uniform measure on a finite length interval with a codimension‐one base. As our two main applications, we consider the case when the base is the flat torus R2/2Z2$\mathbb {R}^2 / 2 \mathbb {Z}^2$ and the standard Gaussian measure
Emanuel Milman
wiley   +1 more source

On Bounds for Norms of Reparameterized ReLU Artificial Neural Network Parameters: Sums of Fractional Powers of the Lipschitz Norm Control the Network Parameter Vector

open access: yesMathematical Methods in the Applied Sciences, Volume 49, Issue 4, Page 2135-2160, 15 March 2026.
ABSTRACT It is an elementary fact in the scientific literature that the Lipschitz norm of the realization function of a feedforward fully connected rectified linear unit (ReLU) artificial neural network (ANN) can, up to a multiplicative constant, be bounded from above by sums of powers of the norm of the ANN parameter vector.
Arnulf Jentzen, Timo Kröger
wiley   +1 more source

Norms of some operators between weighted-type spaces and weighted Lebesgue spaces

open access: yesAIMS Mathematics, 2023
<abstract><p>We calculate the norms of several concrete operators, mostly of some integral-type ones between weighted-type spaces of continuous functions on several domains. We also calculate the norm of an integral-type operator on some subspaces of the weighted Lebesgue spaces.</p></abstract>
openaire   +2 more sources

Home - About - Disclaimer - Privacy