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Fast and exact simulation of complex-valued stationary Gaussian processes through embedding circulant matrix. (English) Zbl 07498946

Summary: This article is concerned with the study of the embedding circulant matrix method to simulate stationary complex-valued Gaussian sequences. The method is, in particular, shown to be well-suited to generate circularly symmetric stationary Gaussian processes. We provide simple conditions on the complex covariance function ensuring the theoretical validity of the minimal embedding circulant matrix method. We show that these conditions are satisfied by many examples and illustrate the simulation algorithm. In particular, we present a simulation study involving the circularly symmetric fractional Gaussian noise, a model introduced in this article. Supplementary material for this article is available online.

MSC:

62-XX Statistics

Software:

nag; R; SimEstFBM; rmarkdown; NAG

References:

[1] Allaire, J.; Cheng, J.; Xie, Y.; McPherson, J.; Chang, W.; Allen, J.; Wickham, H.; Atkins, A.; Hyndman, R., rmarkdown: Dynamic Documents for R, r Package Version 0.9.6 (2016)
[2] Amblard, P.-O.; Coeurjolly, J.-F.; Lavancier, F.; Philippe, A., Séminaires et Congrés, Self-similar Processes and their Applications, 28, Basic Properties of the Multivariate Fractional Brownian Motion,, 65-87 (2013)
[3] Amblard, P.-O.; Gaeta, M.; Lacoume, J.-L., Statistics for Complex Variables and Signals—Part II: signals, Signal Processing, 53, 15-25 (1996) · Zbl 0864.94006
[4] Barry, J.; Lee, E.; Messerschmitt, D., Digital Communication (2004), New York: Springer Science+Business Media, New York
[5] Berg, C.; Forst, G., Potential Theory on Locally Compact Abelian Groups, 87 (1978), New York: Springer Science & Business Media, New York
[6] Brockwell, P.; Davis, R., Time Series: Theory and Methods (1991), New York: Springer Verlag, New York · Zbl 0709.62080
[7] Chan, G.; Wood, A., Simulation of Stationary Gaussian Vector Fields, Statistics and Computing, 9, 265-268 (1999)
[8] Chandna, S.; Walden, A., Simulation Methodology for Inference on Physical Parameters of Complex Vector-Valued Signals, IEEE Transactions on Signal Processing, 61, 5260-5269 (2013)
[9] Coeurjolly, J.-F., Simulation and Identification of the Fractional Brownian Motion: A Bibliographical and Comparative Study, Journal of Statistical Software, 5, 1-53 (2000)
[10] Coeurjolly, J.-F.; Amblard, P.-O.; Achard, S., Wavelet Analysis of the Multivariate Fractional Brownian Motion, ESAIM: Probability and Statistics, 17, 592-604 (2013) · Zbl 1293.42038
[11] Coeurjolly, J.-F.; Porcu, E., Properties and Hurst Exponent Estimation of the Circularly-Symmetric Fractional Brownian Motion, Statistics and Probability Letters, 128, 21-27 (2017) · Zbl 1379.60044
[12] Craigmile, P., Simulating a Class of Stationary Gaussian Processes using the Davies-Harte Algorithm, with Application to Long-Memory Processes, Journal of Time Series Analysis, 24, 505-511 (2003) · Zbl 1035.68131
[13] Curtis, T., Adaptive Methods in Underwater Acoustics, Digital Signal Processing for Sonar,, 583-605 (1985), New York: Springer, New York
[14] Davies, R.; Harte, D., Tests for Hurst Effect, Biometrika, 74, 95-101 (1987) · Zbl 0612.62123
[15] Davies, T.; Bryant, D., On Circulant Embedding for Gaussian Random Fields in, Journal of Statistical Software, 55, 1-21 (2013)
[16] Didier, G.; Pipiras, V., Integral Representations of Operator Fractional Brownian Motions, Bernouilli, 17, 1-33 (2011) · Zbl 1284.60079
[17] Dietrich, C.; Newsam, G., Fast and Exact Simulation of Stationary Gaussian Processes Through Circulant Embedding of the Covariance Matrix, SIAM Journal of Scientific Computing, 18, 1088-1107 (1997) · Zbl 0890.65149
[18] Douglas, N.; Furrer, R.; Paige, J.; Sain, S., Fields: Tools for Spatial Data, R Package Version 8.10 (2015)
[19] Doukhan, P.; Oppenheim, G.; Taqqu, M., Theory and Applications of Long-Range Dependence (2003), New York: Springer Science & Business Media, New York · Zbl 1005.00017
[20] Dunmire, B.; Beach, K.; Labs, K.; Plett, M.; Strandness, D., Cross-Beam Vector Doppler Ultrasound for Angle-Independent Velocity Measurements, Ultrasound in Medicine & Biology, 26, 1213-1235 (2000)
[21] Dunn, O., Estimation of the Means of Dependent Variables, The Annals of Mathematical Statistics, 29, 1095-1111 (1958) · Zbl 0092.36702
[22] ———, Confidence Intervals for the Means of Dependent, Normally Distributed Variables, Journal of the American Statistical Association, 54, 613-621 (1959) · Zbl 0111.15805
[23] Gneiting, T.; Ševčiková, H.; Percival, D.; Schlather, M.; Jiang, Y., Fast and Exact Simulation of Large Gaussian Lattice Systems in : Exploring the Limits, Journal of Computational and Graphical Statistics, 15, 483-501 (2012)
[24] Gray, R., Toeplitz and Circulant Matrices: A Review, Foundations and Trends in Communications and Information Theory, 2, 155-239 (2006) · Zbl 1115.15021
[25] Helgason, H.; Kechagias, S.; Pipiras, V., Convex Optimization and Feasible Circulant Matrix Embeddings in Synthesis of Stationary Gaussian Fields, Journal of Computational and Graphical Statistics, 25, 1158-1175 (2016)
[26] Helgason, H.; Pipiras, V.; Abry, P., Fast and Exact Synthesis of Stationary Multivariate Gaussian Time Series using Circulant Embedding, Signal Processing, 91, 1123-1133 (2011) · Zbl 1221.62123
[27] ———, Smoothing Windows for the Synthesis of Gaussian Stationary Random Fields using Circulant Matrix Embedding, Journal of Computational and Graphical Statistics, 23, 616-635 (2014)
[28] Hosking, J., Fractional Differencing, Biometrika, 68, 165-176 (1981) · Zbl 0464.62088
[29] Lilly, J.; Sykulski, A.; Early, J.; Olhede, S., Fractional Brownian motion, the Matern Process, and Stochastic Modeling of Turbulent Dispersion,, arXiv preprint arXiv:1605.01684 (2016)
[30] Mandelbrot, B.; Van Ness, J., Fractional Brownian Motions, Fractional Noises and Applications, SIAM Review, 10, 422-437 (1968) · Zbl 0179.47801
[31] Numerical Algorithms Group., NAG Fortran Library Manual, Mark 16: F03-F06, 6 (1993), Oxford, U.K.: Numerical Algorithms Group, Oxford, U.K.
[32] Percival, D., Exact Simulation of Complex-Valued Gaussian Stationary Processes via Circulant Embedding, Signal Processing, 86, 1470-1476 (2006) · Zbl 1172.94540
[33] R Core Team, R: A Language and Environment for Statistical Computing (2016), Vienna, Austria: R Foundation for Statistical Computing, Vienna, Austria
[34] Stein, M., Fast and Exact Simulation of Fractional Brownian Surfaces, Journal of Computational and Graphical Statistics, 11, 587-599 (2002)
[35] Sykulski, A.; Percival, D., Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding,, arxiv.org/abs/1605.05278 (2016)
[36] Tobar, F.; Turner, R., 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Modelling of Complex Signals using Gaussian Processes,, 2209-2213 (2015), IEEE
[37] Tong, Y., Probability Inequalities in Multivariate Distributions (1982), New York: Academic Press, New York
[38] Wood, A.; Chan, G., Simulation of Stationary Gaussian Processes in, Journal of Computational and Graphical Statistics, 3, 409-432 (1994)
[39] Zygmund, A., Trigonometric Series, II (2002), Cambridge: Cambridge University Press, Cambridge · Zbl 1084.42003
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