Results 71 to 80 of about 4,141 (227)

Testing Distributional Granger Causality With Entropic Optimal Transport

Journal of Time Series Analysis, EarlyView.
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley   +1 more source

Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression

Journal of Applied Mathematics, 2012
We study learning algorithms generated by regularization schemes in reproducing kernel Hilbert spaces associated with an ϵ-insensitive pinball loss. This loss function is motivated by the ϵ-insensitive loss for support vector regression and the pinball ...
Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou
doaj   +1 more source

Climate change and crop resilience: harnessing metabolomics for predicting stress tolerance

New Phytologist, EarlyView.
Summarised methodology for metabolite biomarker discovery and genomic targets selection for those metabolites to predict high‐throughput phenotypic and agronomic traits of interest for direct uptake in breeding programmes. Summary Global warming is driving climate change to levels not experienced since the advent of agriculture, primarily due to ...
Agyeya Pratap, Arash Fazeli, Ali Bandehagh, Nicolas L. Taylor   +3 more
wiley   +1 more source

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

Abstract 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

Cyclicity in Reproducing Kernel Hilbert Spaces of Analytic Functions [PDF]

Computational Methods and Function Theory, 2014
We introduce a large family of reproducing kernel Hilbert spaces $\mathcal{H} \subset \mbox{Hol}(\mathbb{D})$, which include the classical Dirichlet-type spaces $\mathcal{D}_α$, by requiring normalized monomials to form a Riesz basis for $\mathcal{H}$. Then, after precisely evaluating the $n$-th optimal norm and the $n$-th approximant of $f(z)=1-z$, we
Fricain, Emmanuel, Mashreghi, Javad, Seco, Daniel   +2 more
openaire   +4 more sources

Semi reproducing kernel hilbert spaces and mixed precision computation. [PDF]

, 2018
Positive definite and conditionally positive definite functions are widely used in interpolation and smoothing problems, particularly when the data is scattered.
Garing, Ronald
core   +1 more source

Repelled Point Processes With Application to Numerical Integration

Scandinavian Journal of Statistics, EarlyView.
ABSTRACT We look at Monte Carlo numerical integration from a stochastic geometry point of view. While crude Monte Carlo estimators relate to linear statistics of a homogeneous Poisson point process (PPP), linear statistics of more regularly spread point processes can yield unbiased estimators with faster‐decaying variance, and thus lower integration ...
Diala Hawat, Gabriel Mastrilli, Rémi Bardenet, Raphaël Lachièze‐Rey   +3 more
wiley   +1 more source

Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space

Big Data and Cognitive Computing
Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives, such as invariance to augmentations, variance preservation, and feature decorrelation, without requiring labels.
M. Hadi Sepanj, Benyamin Ghojogh, Saed Moradi, Paul Fieguth   +3 more
doaj   +1 more source

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

Advances 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, Nadir Djeddi, Mohammed Al-Smadi, Shrideh Al-Omari, Shaher Momani, Andreea Fulga   +5 more
doaj   +1 more source

Sparse Minimum Redundancy Maximum Relevance for Feature Selection

Scandinavian Journal of Statistics, EarlyView.
ABSTRACT We propose a feature screening method that integrates both feature–feature and feature–target relationships. Inactive features are identified via a penalized minimum Redundancy Maximum Relevance (mRMR) procedure, which is the continuous version of the classical mRMR penalized by a non‐convex regularizer, and where the parameters estimated as ...
Peter Naylor, Benjamin Poignard, Héctor Climente‐González, Makoto Yamada   +3 more
wiley   +1 more source