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Likelihood Ratio Test for the Equivalence of Two Autoregressive Moving- Average Time Series

Authors: Gerald W. Swope; NAVAL UNDERSEA WARFARE CENTER NEWPORT DIV RI
 
Abstract: To passively detect quiet sources, future sonar systems will require more sensors, which may contribute to operator overload. The methods described in this report have the potential to automatically determine if two sonar tracks, for example, correspond to the same source, thereby improving operator performance. Specifically, a likelihood ratio test for the equivalence of two autoregressive moving average (ARMA) time series is derived. This test investigates the structural characteristics of the two time series through the ARMA parameters. Four cases of this test are presented for examining the ARMA parameters, series means, and/or innovations variances. The autoregressive (AR) time series is treated separately, not only because AR parameters are easier to estimate, but also because many time series can be characterized by an AR process. Monte Carlo analysis has shown that the likelihood ratio test has a good fit to the chi square distribution, with degrees of freedom equal to the number of parameters being tested.

Limitations: APPROVED FOR PUBLIC RELEASE
Description: Final rept.
Pages: 30
Report Date: 15 SEP 1999
Report Number: A995073
Keywords relating to this report:
*MAXIMUM LIKELIHOOD ESTIMATION
*SIGNAL PROCESSING
*SONAR SIGNALS
CHI SQUARE TEST
MATHEMATICAL MODELS
MONTE CARLO METHOD
REGRESSION ANALYSIS
TIME SERIES ANALYSIS
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