Results 71 to 80 of about 14,885,883 (209)
Journal 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
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On the weak limit of compact operators on the reproducing kernel Hilbert space and related questions
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2016 By applying the so-called Berezin symbols method we prove a Gohberg- Krein type theorem on the weak limit of compact operators on the non- standard reproducing kernel Hilbert space which essentially improves the similar results of Karaev [5]: We also in ...
Saltan Suna
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Mathematical Modelling and Analysis, 2015 An iterative method is discussed with respect to its effectiveness and capability of solving singular nonlinear Lane-Emden type equations using reproducing kernel Hilbert space method combined with the Picard iteration.
Babak Azarnavid +2 more
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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
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Abstract and Applied Analysis, 2012 This paper investigates the numerical solution of nonlinear Fredholm-Volterra integro-differential equations using reproducing kernel Hilbert space method. The solution 𝑢(𝑥) is represented in the form of series in the reproducing kernel space.
Omar Abu Arqub +2 more
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Reproducing Kernel Method with Global Derivative
Journal of Function Spaces, 2023 Ordinary differential equations describe several phenomena in different fields of engineering and physics. Our aim is to use the reproducing kernel Hilbert space method (RKHSM) to find a solution to some ordinary differential equations (ODEs) that are ...
Nourhane Attia +2 more
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Reproducing Kernel Hilbert Space Methods for wide-sense self-similar Processes
The Annals of Applied Probability, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nuzman, Carl J., Poor, H. Vincent
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Application of Reproducing Kernel Hilbert Space Method for Solving a Class of Nonlinear Integral Equations [PDF]
Mathematical Problems in Engineering, 2017 A new approach based on the Reproducing Kernel Hilbert Space Method is proposed to approximate the solution of the second‐kind nonlinear integral equations. In this case, the Gram‐Schmidt process is substituted by another process so that a satisfactory result is obtained. In this method, the solution is expressed in the form of a series.
Sedigheh Farzaneh Javan +2 more
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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
Science and Technology IndonesiaReproducing Kernel Hilbert Space (RKHS) is a Hilbert space consisting of functions that can be represented or reproduced by a kernel function. The development of data science has made RKHS a method that refers to an approach or technique using the ...
Bernadhita Herindri Samodera Utami +3 more
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