Results 61 to 70 of about 16,869 (201)
International Statistical Review, EarlyView.Summary Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual distance covariance is bounded, but that its breakdown value is zero.
Sarah Leyder +2 more
wiley +1 more source
A Discretized Tikhonov Regularization Method for a Fractional Backward Heat Conduction Problem
Abstract and Applied Analysis, 2014 We propose a numerical reconstruction method for solving a time-fractional backward heat conduction problem. Based on the idea of reproducing kernel approximation, we reconstruct the unknown initial heat distribution from a finite set of scattered ...
Zhi-Liang Deng, Xiao-Mei Yang
doaj +1 more source
INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES [PDF]
International Journal of Computational Intelligence Systems, 2012 In this paper, information energy metric (IEM) is obtained by similarity computing for high-dimensional samples in a reproducing kernel Hilbert space (RKHS).
Songhua Liu, Junying Zhang, Caiying Ding
doaj +1 more source
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
, 2017 Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series.
A Caponnetto +13 more
core +2 more sources
Boletim da Sociedade Paranaense de Matemática, 2022 The aim of this paper is to extend the application of the reproducing kernel Hilbert space method (RKHSM) to solve linear and non-linear fuzzy integro-differential equations of fractional order under Caputo's H-differentiability. The analytic and approximate solutions are given in series form in term of their parametric form in the space $W_2^2 [a,b ...
Shatha Hasan +3 more
openaire +2 more sources
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
wiley +1 more source
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
doaj +1 more source
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
doaj +1 more source
Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, EarlyView.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
Reproducing Kernel Method for Solving Nonlinear Differential-Difference Equations
Abstract and Applied Analysis, 2012 On the basis of reproducing kernel Hilbert spaces theory, an iterative algorithm for solving some nonlinear differential-difference equations (NDDEs) is presented.
Reza Mokhtari +2 more
doaj +1 more source