Exponential Convergence of Gevers-Wouters Algorithm for Moving Average Model Parameter Estimation
Zili Deng · Science Technology and Engineer · 2005
The parameter estimation problem to the invertible vector moving average (MA) model essentially is a matrix spectral factorization problem. By the Kalman filtering method, based on the transformation between the vector MA model and the state space model, the consistence and exponential convergence of the Gevers-Wouters algorithm for matrix spectral factorization is proved, and it is proved that the convergence rate is determined by the zeros of the determinant of the MA polynomial matrix. When these zeros points are not approximate to the unit circle, the Gevers-Wouters algorithm can quickly give the vector MA parameter estimates with the higher accuracy, and provides a fast and efficient spectral factorization tool.