Results 101 to 110 of about 45,837 (216)
ε-Pseudo Chebyshev and ε-quasi Chebyshev subspaces of Banach spaces
Analysis in Theory and Applications, 2004 We will define and characterize e-pseudo Chebyshev and e-quasi Chebyshev subspaces of Banach spaces. We will prove that a closed subspace W is e-pseudo Chebyshev if and only if W is e-quasi Chebyshev.
openaire +1 more source
Piecewise Dynamic Time Warping-Based Subsequence Matching in Data Stream
IEEE AccessDynamic Time Warping (DTW) is effective for subsequence matching but is limited by its high computational cost. To address this challenge, we propose a method that adaptively segments data streams and extracts Chebyshev features for piecewise ...
Qinglin Cai +3 more
doaj +1 more source
Differential Quadrature Solution of Hyperbolic Telegraph Equation
Journal of Applied Mathematics, 2012 Differential quadrature method (DQM) is proposed for the numerical solution of one- and two-space dimensional hyperbolic telegraph equation subject to appropriate initial and boundary conditions.
B. Pekmen, M. Tezer-Sezgin
doaj +1 more source
Sequential Outlier Detection in Nonstationary Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT A novel method for sequential outlier detection in nonstationary time series is proposed. The method tests the null hypothesis of “no outlier” at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extreme‐value theory. The asymptotic properties of the test statistic are studied
Florian Heinrichs +2 more
wiley +1 more source
Noncommutative Chebyshev inequality involving the Hadamard product
, 2018 We present several operator extensions of the Chebyshev inequality for Hilbert space operators. The main version deals with the synchronous Hadamard property for Hilbert space operators.
Bakherad, Mojtaba +1 more
core
Confidence Intervals for Price Discovery
Oxford Bulletin of Economics and Statistics, EarlyView.ABSTRACT This paper discusses asymptotic and bootstrap confidence intervals for multivariate permanent‐transitory decompositions of cointegrated vector autoregressive I(1) systems, with a focus on price discovery. Alternative estimators of the permanent components are compared in terms of efficiency also under separable linear restrictions on the ...
Heino Bohn Nielsen +2 more
wiley +1 more source
International Transactions in Operational Research, Volume 33, Issue 4, Page 2499-2534, July 2026.Abstract This paper presents a two‐stage model for planning a renewable energy portfolio by balancing economic, social and environmental sustainability goals. The first stage addresses a multi‐objective problem where conflictive impacts generated by the energy portfolios should be optimised according to the corresponding economic, social or ...
Amelia Bilbao‐Terol +2 more
wiley +1 more source
Fourier Mass Lower Bounds for Batchelor‐Regime Passive Scalars
Communications on Pure and Applied Mathematics, Volume 79, Issue 6, Page 1449-1466, June 2026.ABSTRACT Batchelor predicted that a passive scalar ψν$\psi ^\nu$ with diffusivity ν$\nu$, advected by a smooth fluid velocity, should typically have Fourier mass distributed as |ψ̂ν|2(k)≈|k|−d$|\widehat{\psi }^\nu |^2(k) \approx |k|^{-d}$ for |k|≪ν−1/2$|k| \ll \nu ^{-1/2}$.
William Cooperman, Keefer Rowan
wiley +1 more source
An efficient fractional polynomial method for space fractional diffusion equations
Ain Shams Engineering Journal, 2018 In this paper, we develop a new approximation technique for solving space fractional diffusion equation. The method of solution is based on fractional order Legendre function with the concept of Caputo’s definition of fractional derivatives.
K. Krishnaveni +3 more
doaj +1 more source
Personalized Differential Privacy for Ridge Regression Under Output Perturbation
Naval Research Logistics (NRL), Volume 73, Issue 4, Page 525-537, June 2026.ABSTRACT The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). Traditional DP enforces a uniform privacy level ε$$ \varepsilon $$, which bounds the maximum privacy loss that each data point in the dataset is allowed to incur.
Krishna Acharya +3 more
wiley +1 more source