Generating correlated gaussian random fields by orthogonal polynomial approximations to the square root of the covariance matrix

DOI10.1080/00949659408811601zbMath0833.62092OpenAlexW2052757513MaRDI QIDQ4864223

G. N. Newsam, C. R. Dietrich

Publication date: 29 January 1996

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659408811601

zbMATH Keywords

orthogonal polynomialserror analysisMonte Carlo simulationmatrix square rootrates of convergenceChebyshev polynomial expansionsmatrix polynomialminimax polynomial approximationsoptimal weighted least squares solutionsquasi-Jacobi polynomial expansions

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Best approximation, Chebyshev systems (41A50) Geostatistics (86A32) Probabilistic methods, stochastic differential equations (65C99)

Related Items (1)

Efficient generation of conditional simulations by Chebyshev matrix polynomial approximations to the symmetric square root of the covariance matrix

Uses Software

• Mathematica

Cites Work

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• Computationally exploitable structure of covariance matrices and generalized convariance matrices in spatial models
• Eigenvalues of Differentiable Positive Definite Kernels
• On the Sharpness of Weyl’s Estimates for Eigenvalues of Smooth Kernels, II
• Best uniform rational approximation of 𝑥^{𝛼} on [0,1]
• Computationally efficient cholesky factorization of a covariance matrix with block toeplitz structure
• Divide-and-Conquer Solutions of Least-Squares Problems for Matrices with Displacement Structure

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