Results 111 to 120 of about 2,360 (218)

Spatial depth for data in metric spaces

Scandinavian Journal of Statistics, Volume 53, Issue 2, Page 684-711, June 2026.
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

Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data

, 2021
Imputation and propensity score weighting are two popular techniques for handling missing data. We address these problems using the regularized M-estimation techniques in the reproducing kernel Hilbert space.
Kim, Jae Kwang, Wang, Hengfang
core  

Reproducing kernel hilbert space methods for cad tools

, 2004
The review of known RKHS-methods for analysis of current state in science investigations is represented. The place of Series Summation Method in Reproducing Kernel Hilbert Space (RKHS) is determined. The new results obtained by this method are discussed.
Gowher, Malik, Chumachenko, S. V., Khawar, Parvez   +2 more
core  

Numerical Simulation of the Kudryashov–Sinelshchikov Equation for Modeling Pressure Waves in Liquids with Gas Bubbles

Mathematics
The Kudryashov–Sinelshchikov equation (KSE) is crucial in modeling pressure waves in liquids containing gas bubbles, capturing both nonlinear wave phenomena and dispersion effects.
Gayatri Das, Bibekananda Sitha, Rajesh Kumar Mohapatra, Predrag Stanimirović, Tzung-Pei Hong   +4 more
doaj   +1 more source

Semantic Models for Machine Learning [PDF]

, 2006
In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications.
Hardoon, David R, Hardoon, David Roi
core  

Exploring reproducing kernel hilbert space and its application to survival data. [PDF]

, 2011
In this paper, we will construct a kernel K(x,y) and verify that it is a reproducing kernel in Hilbert space (RKHS). The basic facts and important properties of an RKHS will be reviewed.
Aljawadi, Bader, Abdul Manaf, Nur'azah, Ibragimov, Gafurjan, Abu Bakar , Mohd Rizam   +3 more
core  

Existence of Unique Solutions to the Telegraph Equation in Binary Reproducing Kernel Hilbert Spaces

, 2020
We demonstrate the existence of a unique solution to a nonhomogeneous telegraph initial/boundary value problem on the unit square in an appropriate binary reproducing kernel Hilbert space which depends on the smoothness of the driver.
Grow, David E., Akgul, Ali
core   +1 more source

Representation for the reproducing kernel Hilbert space method for a nonlinear system

, 2017
We apply the reproducing kernel Hilbert space method to a nonlinear system in this work. We utilize this technique to overcome the nonlinearity of the problem. We obtain accurate results. We demonstrate our results by tables and figures. We prove the efficiency of the method.
KARATAS AKGÜL, Esra, AKGÜL, Ali, KHAN, Yasir, BALEANU, Dumitru   +3 more
openaire   +3 more sources

REPRODUCING KERNEL HILBERT SPACE METHOD]{REPRESENTATION FOR THE REPRODUCING KERNEL HILBERT SPACE METHOD FOR A NONLINEAR SYSTEM

Hacettepe Journal of Mathematics and Statistics, 2018
Esra Karataş Akgül, Ali Akgül, Yasir Khan, Dumitru Baleanu   +3 more
openaire   +1 more source

Finite and Infinite Dimensional Reproducing Kernel Hilbert Space Approach for Bagley–Torvik Equation

In this paper, two different numerical approaches are presented in finite dimensional and infinite dimensional reproducing kernel Hilbert spaces for the fractional order Bagley–Torvik equation with boundary conditions.
Şenol, Mehmet, Saldır, Onur, Sakar, Mehmet Giyas, Ata, Ayşe   +3 more
core   +1 more source