RECURSIVE SIMULATION OF STATIONARY MULTIVARIATE RANDOM PROCESSES - PART I.

Marc Mignolet; P. D. Spanos

RECURSIVE SIMULATION OF STATIONARY MULTIVARIATE RANDOM PROCESSES - PART I.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 are 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 language	English (US)
Title of host publication	American Society of Mechanical Engineers (Paper)
Publisher	ASME, USA, 7p
State	Published - 1987
Externally published	Yes

ASJC Scopus subject areas

Mechanical Engineering

Cite this

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title = "RECURSIVE SIMULATION OF STATIONARY MULTIVARIATE RANDOM PROCESSES - PART I.",

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 are 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.",

author = "Marc Mignolet and Spanos, {P. D.}",

year = "1987",

language = "English (US)",

booktitle = "American Society of Mechanical Engineers (Paper)",

publisher = "ASME, USA, 7p",

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PY - 1987

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N2 - 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 are 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.

AB - 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 are 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.

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M3 - Conference contribution

AN - SCOPUS:0023591012

BT - American Society of Mechanical Engineers (Paper)

PB - ASME, USA, 7p

ER -

RECURSIVE SIMULATION OF STATIONARY MULTIVARIATE RANDOM PROCESSES - PART I.

Abstract

ASJC Scopus subject areas

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