RECURSIVE SIMULATION OF STATIONARY MULTIVARIATE RANDOM PROCESSES - PART I.

Marc Mignolet, P. D. Spanos

Research output: Contribution to journalArticle

56 Citations (Scopus)

Abstract

A unified approach is presented in determining autoregressive moving average (ARMA) algorithms for simulating realizations of multivariate random processes with a specified (target) spectral matrix. The ARMA algorithms were derived by relying on a prior autoregressive (AR) approximation of the target matrix. Several AR to ARMA procedures are formulated by minimizing a frequency domain error. Equations which can lead to a convenient computation of the ARMA matrix coefficients for a particular problem are given. Finally, the features of the various procedures are critically assessed.

Original languageEnglish (US)
Pages (from-to)674-680
Number of pages7
JournalJournal of Applied Mechanics, Transactions ASME
Volume54
Issue number3
StatePublished - Sep 1987

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autoregressive moving average
random processes
Random processes
matrices
simulation
coefficients
approximation

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials

Cite this

RECURSIVE SIMULATION OF STATIONARY MULTIVARIATE RANDOM PROCESSES - PART I. / Mignolet, Marc; Spanos, P. D.

In: Journal of Applied Mechanics, Transactions ASME, Vol. 54, No. 3, 09.1987, p. 674-680.

Research output: Contribution to journalArticle

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