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Simulation of Real Discrete Time Gaussian Multivariate Stationary Processes with Given Spectral Densities
Author(s) -
Azimmohseni M.,
Soltani A. R.,
Khalafi M.
Publication year - 2015
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12125
Subject(s) - univariate , mathematics , convergence (economics) , multivariate statistics , discrete time and continuous time , gaussian , matrix (chemical analysis) , rate of convergence , spectral density , stationary process , gaussian process , bounded function , statistical physics , statistics , mathematical analysis , computer science , physics , computer network , channel (broadcasting) , materials science , quantum mechanics , economics , composite material , economic growth
In this article we establish a simulation procedure to generate values for a real discrete time multivariate stationary process, based on a factor of spectral density matrix. We prove the convergence of the simulator, at each time epoch, to the actual process, and provide the corresponding rate of convergence. We merely assume that the spectral density matrix is continuous and of bounded variation. By using the positive root factor, we provide an extended version for the Sun and Chaika ([Sun TC, 1997]) simulator, for real univariate stationary processes.