
Accession Number : ADA186561
Title : Short Data Length Effects in an Asymptotically Efficient ARMA (AutoRegressive Moving Average) Spectral Estimator.
Descriptive Note : Technical rept.,
Corporate Author : OHIO STATE UNIV COLUMBUS ELECTROSCIENCE LAB
Personal Author(s) : Carriere, C ; Moses, R L
PDF Url : ADA186561
Report Date : Jul 1987
Pagination or Media Count : 84
Abstract : The short data length behaviour of a recently proposed computationally efficient approximate maximum likelihood estimation algorithm is studied through Monte Carlo simulations. It is found that short data lengths combined with a large number of instruments results in very high variances, especially when the process being estimated has zeros near the unit circle. Several modifications of the algorithm are considered to reduce the problem mentioned above. First, a version which is recursive in the number of instruments and which adaptively chooses the number of instruments and postiterations is developed. A second modification uses a stabilized version of the estimated denominator polynomial. A version that forces the numerator estimate to non negative definite is considered, but it fails to give major improvements over the original algorithm. Finally, using overdetermined Yule Walker equations instead of the minimal number is found to markedly improve the quality of the estimates.
Descriptors : *ALGORITHMS, *MAXIMUM LIKELIHOOD ESTIMATION, *REGRESSION ANALYSIS, EQUATIONS, ESTIMATES, MODIFICATION, MONTE CARLO METHOD, QUALITY, SIMULATION, MATHEMATICAL MODELS, ASYMPTOTIC NORMALITY, POLYNOMIALS, WHITE NOISE, SIGNALS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE