Title :
Threshold bounds in SVD and a new iterative algorithm for order selection in AR models
Author :
Konstantinides, Konstantinos
Author_Institution :
Hewlett-Packard Lab., Palo Alto, CA, USA
fDate :
5/1/1991 12:00:00 AM
Abstract :
The problem of order determination of AR (autoregressive) models using singular value decomposition (SVD) is reexamined from a statistical point of view. Thresholds for distinguishing between significant and nonsignificant singular values are derived, and a novel iterative algorithm for order selection in AR models is presented. Simulation results show the technique to be very effective when a small number of samples is available
Keywords :
iterative methods; matrix algebra; statistics; AR models; autoregressive models; iterative algorithm; order determination; simulation results; singular value decomposition; statistics; threshold bounds; Apertures; Array signal processing; Iterative algorithms; Maximum likelihood detection; Maximum likelihood estimation; Optical signal processing; Parameter estimation; Sensor arrays; Signal processing; Speech processing;
Journal_Title :
Signal Processing, IEEE Transactions on
