Results 101 to 110 of about 2,360 (218)
Utilization of reproducing kernel hilbert space method on the survival data of leukemia patients [PDF]
, 2015 The theory of reproducing kernel has been recognized as a useful instrument in several areas of mathematical research. This has been verified by researches done by Aronszajn (1950), Berlinet et al. (2003), Burbea (1976), Hille (1972), Li et al. and Wahba
Ibragimov, Gafurjan +2 more
core
Reproducing kernel Hilbert space methods for modelling the discount curve
We consider the theory of bond discounts, defined as the difference between the terminal payoff of the contract and its current price. Working in the setting of finite-dimensional realizations in the HJM framework, under suitable notions of no-arbitrage, the admissible discount curves take the form of polynomial, exponential functions.
Celary, Andreas +2 more
openaire +2 more sources
A numerical approach for solving the high-order nonlinear singular Emden–Fowler type equations
Advances in Difference Equations, 2018 Reproducing kernel Hilbert space method (RKHSM) is an analytical technique, which can overcome the difficulty at the singular point of non-homogeneous, linear singular initial value problems; especially when the singularity appears on the right-hand side
Atta Dezhbord +2 more
doaj +1 more source
On the Foundational Arguments of Sufficient Dimension Reduction
WIREs Computational Statistics, Volume 18, Issue 2, June 2026.Contemporary Sufficient Dimension Reduction, a versatile method for extracting material information from data, can serve as a preprocessor for classical modeling and inference, or as a standalone theory that leads directly to statistical inference. ABSTRACT Sufficient dimension reduction (SDR) refers to supervised methods of dimension reduction that ...
R. Dennis Cook
wiley +1 more source
Full Details of Solving Initial Value Problems by Reproducing Kernel Hilbert Space Method
, 2015 In this paper we solve in full details an initial value problem by reproducing kernel Hilbert space method and we notice that this solution is close to the exact solution.
AL- Azzawi, Saad N. +2 more
core
Explicit recursivity into reproducing kernel Hilbert spaces
, 2011 This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces (RKHS). Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define model recursivity in the ...
Gustavo Camps-Valls +5 more
core +1 more source
Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection
Entropy, 2018 We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to ...
Gabriel Martos +3 more
doaj +1 more source
Information Flow in Geophysical Systems
Journal of Advances in Modeling Earth Systems, Volume 18, Issue 6, June 2026.Abstract We present a new framework for analyzing the evolution of information in geophysical systems. Understanding how information, and its counterpart, uncertainty, propagates is central to predictability studies and has significant implications for applications such as forecast uncertainty quantification and risk management. It also offers valuable
P. J. van Leeuwen
wiley +1 more source
A Reproducing Kernel Method for Solving a Class of Nonlinear Systems of PDEs
Mathematical Modelling and Analysis, 2014 This paper is concerned with a technique for solving a class of nonlinear systems of partial differential equations (PDEs) in the reproducing kernel Hilbert space. The analytical solution is represented in the form of series. An iterative method is given
Maryam Mohammadi, Reza Mokhtari
doaj +1 more source
The theory and application of penalized methods or Reproducing Kernel Hilbert Spaces made easy
Statistics Surveys, 2012 The popular cubic smoothing spline estimate of a regression function arises as the minimizer of the penalized sum of squares $\sum_j(Y_j - μ(t_j))^2 + λ\int_a^b [μ"(t)]^2 dt$, where the data are $t_j,Y_j$, $j=1,..., n$. The minimization is taken over an infinite-dimensional function space, the space of all functions with square integrable second ...
openaire +3 more sources