Results 71 to 80 of about 2,581 (224)
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
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 +3 more
wiley +1 more source
Nonlinear Dimensionality Reduction Based on HSIC Maximization
IEEE Access, 2018 Hilbert-Schmidt independence criterion (HSIC) is typically used to measure the statistical dependence between two sets of data. HSIC first transforms these two sets of data into two reproducing Kernel Hilbert spaces (RKHS), respectively, and then ...
Zhengming Ma +3 more
doaj +1 more source
Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator
Entropy, 2020 Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator and its generator, but also the Schrödinger operator.
Stefan Klus +2 more
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 +2 more
openaire +4 more sources
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 +3 more
wiley +1 more source
Certain isometries related to the bilateral laplace transform
Mathematical Modelling and Analysis, 2006 We study certain isometries between Hilbert spaces, which are generated by the bilateral Laplace transform In particular, we construct an analog of the Bargmann‐Fock type reproducing kernel Hilbert space related to this transformation. It is shown
S. B. Yakubovich
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 +3 more
wiley +1 more source
A characterization of multiplication operators on reproducing kernel Hilbert spaces [PDF]
, 2008 In this note, we prove that an operator between reproducing kernel Hilbert spaces is a multiplication operator if and only if it leaves invariant zero sets. To be more precise, it is shown that an operator T between reproducing kernel Hilbert spaces is a
Barbian, Christoph
core +1 more source
How to Match Cognitive Model Predictions With EEG Data
Topics in Cognitive Science, EarlyView.Abstract Reliably identifying relevant brain areas implicated by the simulated activity from cognitive models is still an unsolved problem for cognitive modeling, particularly when matching model output with human electroencephalography (EEG) data. We propose a new method involving postprocessing of ACT‐R module activity and clustered EEG component ...
Kai Preuss +3 more
wiley +1 more source