Results 31 to 40 of about 2,341,583 (290)

Linearization errors in discrete goal-oriented error estimation

open access: yesComputer Methods in Applied Mechanics and Engineering, 2023
This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered.
Brian N. Granzow   +2 more
openaire   +3 more sources

Solution of the SIR epidemic model of arbitrary orders containing Caputo-Fabrizio, Atangana-Baleanu and Caputo derivatives

open access: yesAIMS Mathematics
The main aim of this study was to apply an analytical method to solve a nonlinear system of fractional differential equations (FDEs). This method is the Adomian decomposition method (ADM), and a comparison between its results was made by using a ...
Eman A. A. Ziada   +5 more
doaj   +1 more source

A Perturbed Milne’s Quadrature Rule for n-Times Differentiable Functions with Lp-Error Estimates

open access: yesAxioms, 2023
In this work, a perturbed Milne’s quadrature rule for n-times differentiable functions with Lp-error estimates is derived. One of the most important advantages of our result is that it is verified for p-variation and Lipschitz functions.
Ayman Hazaymeh   +4 more
doaj   +1 more source

Changes in Body Composition in Children and Young People Undergoing Treatment for Acute Lymphoblastic Leukemia: A Systematic Review and Meta‐Analysis

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Ongoing evidence indicates increased risk of sarcopenic obesity among children and young people (CYP) with acute lymphoblastic leukemia (ALL), often beginning early in treatment, persisting into survivorship. This review evaluates current literature on body composition in CYP with ALL during and after treatment.
Lina A. Zahed   +5 more
wiley   +1 more source

Estimation with Norm Regularization [PDF]

open access: yes, 2015
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model.
Banerjee, Arindam   +3 more
core  

A Computational Technique for Solving Three-Dimensional Mixed Volterra–Fredholm Integral Equations

open access: yesFractal and Fractional, 2023
In this article, a novel and efficient approach based on Lucas polynomials is introduced for solving three-dimensional mixed Volterra–Fredholm integral equations for the two types (3D-MVFIEK2).
Amr M. S. Mahdy   +3 more
doaj   +1 more source

Radiotherapy Delivery in Deep Inspiration for Pediatric Patients—Final Results of the Phase II Feasibility Study TEDDI

open access: yesPediatric Blood &Cancer, EarlyView.
Abstract Introduction The TEDDI trial tested the feasibility and reproducibility of deep‐inspiration breath‐hold (DIBH) in pediatric patients referred for radiotherapy. This report presents final results, including patient‐reported outcomes (PRO) and dosimetric comparison of DIBH and free‐breathing (FB).
Daniella Elisabet Østergaard   +11 more
wiley   +1 more source

Error structures and parameter estimation [PDF]

open access: yes, 2006
This article proposes a link between statistics and the theory of Dirichlet forms used to compute errors. The error calculus based on Dirichlet forms is an extension of classical Gauss' approach to error propagation.
Bouleau, Nicolas, Chorro, Christophe
core   +3 more sources

Nonparametric estimation of mean-squared prediction error in nested-error regression models

open access: yes, 2005
Nested-error regression models are widely used for analyzing clustered data. For example, they are often applied to two-stage sample surveys, and in biology and econometrics.
Hall, Peter, Maiti, Tapabrata
core   +1 more source

Estimation error for blind Gaussian time series prediction [PDF]

open access: yes, 2011
We tackle the issue of the blind prediction of a Gaussian time series. For this, we construct a projection operator build by plugging an empirical covariance estimation into a Schur complement decomposition of the projector. This operator is then used to
Espinasse, Thibault   +2 more
core   +3 more sources

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