Results 51 to 60 of about 1,714 (187)

Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley   +1 more source

Numerical Algorithm for the Third-Order Partial Differential Equation with Three-Point Boundary Value Problem

open access: yesAbstract and Applied Analysis, 2014
A numerical method based on the reproducing kernel theorem is presented for the numerical solution of a three-point boundary value problem with an integral condition.
Jing Niu, Ping Li
doaj   +1 more source

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

Numerical solvability of generalized Bagley–Torvik fractional models under Caputo–Fabrizio derivative

open access: yesAdvances in Difference Equations, 2021
This paper deals with the generalized Bagley–Torvik equation based on the concept of the Caputo–Fabrizio fractional derivative using a modified reproducing kernel Hilbert space treatment.
Shatha Hasan   +5 more
doaj   +1 more source

Unveiling sex dimorphism in the healthy cardiac anatomy: Fundamental differences between male and female heart shapes

open access: yesThe Journal of Physiology, EarlyView.
Abstract figure legend We present a shape modelling‐based morphological analysis of sex differences in cardiac anatomy. We conduct our analysis on 456 healthy subjects from the UK Biobank (227M/229F) to uncover sex‐based differences in healthy cardiac morphology.
Beatrice Moscoloni   +4 more
wiley   +1 more source

RKHS approach for signal detection in rotation and scale space random fields [PDF]

open access: yesJournal of Statistical Theory and Applications (JSTA), 2016
Two important papers of Worsley, Siegmund and coworkers consider rotation and scale space random fields for detecting signals in fMRI (functional magnetic resonance imaging) brain images. They use the global maxima of images for detection of a signal. In
K. Shafie, Akbar Abravesh
doaj   +1 more source

Reproducing Kernel Hilbert Spaces and fractal interpolation

open access: yesJournal of Computational and Applied Mathematics, 2011
The main result of this work is to link two fields: fractal interpolation and reproducing kernel Hilbert space. The corresponding spaces of the simple fractal interpolation functions are also reproducing kernel Hilbert spaces, as specific cases. The authors provide the elements for calculating the respective kernel functions for reproducing kernel ...
Bouboulis, P., Mavroforakis, M.
openaire   +2 more sources

Panoramic voltage‐sensitive optical mapping of contracting hearts using cooperative multiview motion tracking with 12 cameras

open access: yesThe Journal of Physiology, EarlyView.
Abstact figure legend A panoramic 3D optical mapping system was developed, enabling imaging of action potential waves across the entire strongly deforming ventricular surface of beating isolated hearts. The system comprises 12 high‐speed cameras and a soccerball‐shaped imaging chamber with 48 light‐emitting diodes (LEDs).
Shrey Chowdhary   +5 more
wiley   +1 more source

Testing Hypotheses of Covariate Effects on Topics of Discourse

open access: yesStatistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 2, April 2026.
ABSTRACT We introduce an approach to topic modeling with document‐level covariates that remains tractable in the face of large text corpora. This is achieved by de‐emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are
Gabriel Phelan, David A. Campbell
wiley   +1 more source

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