Results 111 to 120 of about 4,141 (227)
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
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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
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Berezin number inequalities for operators
Concrete Operators, 2019 The Berezin transform à of an operator A, acting on the reproducing kernel Hilbert space ℋ = ℋ (Ω) over some (non-empty) set Ω, is defined by Ã(λ) = 〉Aǩ λ, ǩ λ〈 (λ ∈ Ω), where k⌢λ=kλ‖kλ‖${\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\frown ...
Bakherad Mojtaba, Garayev Mubariz T.
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Uniform Distribution, Discrepancy, and Reproducing Kernel Hilbert Spaces
Journal of Complexity, 2001 The results are related with numerical integration of functions in a reproducing kernel Hilbert space (RKHS). The authors define a notion of uniform distribution and discrepancy of sequences in an abstract set \(E\) in terms of a RKHS of functions on \(E\). In the case of the finite-dimensional unit cube the discrepancies introduced are closely related
Clemens Amstler, Peter Zinterhof
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Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, Volume 53, Issue 2, Page 684-711, June 2026.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
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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 Hilbert Spaces Over Interval, Circle, and Sphere
, 2021 This thesis discusses the problem of regression models in the field of statistics, and introduces the application of mathematical analysis in statistics.
Hsu, Yu-Chi
core
, 2007 W pracy przedstawiono matematyczny opis sygnałów diagnostycznych przestrzeni Hilberta oraz sposób konstrukcji tej przestrzeni. Podano teorię jąder reprodukujących w zastosowaniu do próbkowania sygnałów diagnostycznych oraz zapis klasycznego twierdzenia o
Syroka, Z.
core
Comportamento estocástico do algoritmo kernel least-mean-square [PDF]
, 2012 Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Algoritmos baseados em kernel têm-se tornado populares no processamento não-linear de sinais.
Parreira, Wemerson Delcio
core
High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space. [PDF]
Math Geosci, 2020 Yao L, Dimitrakopoulos R, Gamache M.
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