TY - JOUR
T1 - Application of fast orthogonal search to linear and nonlinear stochastic systems.
AU - Chon, K H
AU - Korenberg, M J
AU - Holstein-Rathlou, N H
N1 - Keywords: Algorithms; Biomedical Engineering; Computer Simulation; Linear Models; Models, Biological; Nonlinear Dynamics; Physiology; Regression Analysis; Stochastic Processes
PY - 1997
Y1 - 1997
N2 - Standard deterministic autoregressive moving average (ARMA) models consider prediction errors to be unexplainable noise sources. The accuracy of the estimated ARMA model parameters depends on producing minimum prediction errors. In this study, an accurate algorithm is developed for estimating linear and nonlinear stochastic ARMA model parameters by using a method known as fast orthogonal search, with an extended model containing prediction errors as part of the model estimation process. The extended algorithm uses fast orthogonal search in a two-step procedure in which deterministic terms in the nonlinear difference equation model are first identified and then reestimated, this time in a model containing the prediction errors. Since the extended algorithm uses an orthogonal procedure, together with automatic model order selection criteria, the significant model terms are estimated efficiently and accurately. The model order selection criteria developed for the extended algorithm are also crucial in obtaining accurate parameter estimates. Several simulated examples are presented to demonstrate the efficacy of the algorithm.
AB - Standard deterministic autoregressive moving average (ARMA) models consider prediction errors to be unexplainable noise sources. The accuracy of the estimated ARMA model parameters depends on producing minimum prediction errors. In this study, an accurate algorithm is developed for estimating linear and nonlinear stochastic ARMA model parameters by using a method known as fast orthogonal search, with an extended model containing prediction errors as part of the model estimation process. The extended algorithm uses fast orthogonal search in a two-step procedure in which deterministic terms in the nonlinear difference equation model are first identified and then reestimated, this time in a model containing the prediction errors. Since the extended algorithm uses an orthogonal procedure, together with automatic model order selection criteria, the significant model terms are estimated efficiently and accurately. The model order selection criteria developed for the extended algorithm are also crucial in obtaining accurate parameter estimates. Several simulated examples are presented to demonstrate the efficacy of the algorithm.
M3 - Journal article
C2 - 9300103
VL - 25
SP - 793
EP - 801
JO - Annals of Biomedical Engineering
JF - Annals of Biomedical Engineering
SN - 0090-6964
IS - 5
ER -