Spectral Analysis of a Univariate Process with Bad Data Points, via Maximum Entropy and Linear Predictive Techniques
NAVAL UNDERWATER SYSTEMS CENTER NEWPORTRI
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A comparison of several methods for spectral estimation of a univariate process with equi-spaced samples, including maximum entropy, linear predictive, and autoregressive techniques, is made. The comparison is conducted via simulation for situations both with and without bad or missing data points. The case of bad data points required extensions of existing techniques in the literature and is documented fully here in the form of processing equations and FORTRAN programs. It is concluded that the maximum entropy Burg technique is as good as any of the methods considered, for the univariate case. The methods considered are particularly advantageous for short data segments. This report also reviews several available techniques for spectral analysis under different states of knowledge and presents the interrelationships of the various approaches in a consistent notation. Hopefully, this non-rigorous presentation will clarify this method of spectral analysis for readers who are nonexpert in the field.
- Statistics and Probability
- Computer Programming and Software