Spectral analysis based on the canonical autoregressive decomposition
Document Type
Article
Date of Original Version
12-1-1996
Abstract
Statistical inference for mixed spectral problems based on a parametric time series model is studied. The model used herein is based on the canonical autoregressive decomposition (CARD) and represents the underlying random process as the sum of an autoregressive process and sinusoids. Maximum likelihood estimation of the unknown parameters in the model is considered. The entire estimation problem can be shown to require a numerical maximization with respect to only the sinusoidal frequencies. An iterative algorithm to efficiently implement this maximization is presented. This enables us to examine a host of issues associated with a practical implementation of inferential procedures for mixed spectral problems. Some of the topics are accuracy of parameter estimates, selection of model orders, and sensitivity and robustness of the spectral estimates to modeling inaccuracies. The modeling approach, together with the inferential procedures, overcome many of the difficulties encountered in current spectral estimation techniques. © 1996 IEEE.
Publication Title, e.g., Journal
IEEE Transactions on Signal Processing
Volume
44
Issue
7
Citation/Publisher Attribution
Nagesha, Venkatesh, and Steven Kay. "Spectral analysis based on the canonical autoregressive decomposition." IEEE Transactions on Signal Processing 44, 7 (1996): 1719-1733. doi: 10.1109/78.510619.