Title :
Nonlinear maximum likelihood estimation of AR time series
Author :
McWhorter, L. ; Scharf, L.L.
Author_Institution :
Dept. of Electr. & Comput. Eng., Colorado Univ., Boulder, CO, USA
fDate :
31 Oct-2 Nov 1994
Abstract :
Describes an algorithm for finding the exact maximum likelihood (ML) estimators for the parameters of an autoregressive time series. The authors demonstrate that the ML normal equations can be written as an interdependent set of cubic and quadratic equations in the AR polynomial coefficients. They present an algorithm, based on the properties of Sylvester resolvent matrices, that solves this set of non-linear equations for low-order problems
Keywords :
autoregressive processes; matrix algebra; maximum likelihood estimation; nonlinear equations; polynomials; time series; AR time series; Sylvester resolvent matrices; autoregressive time series; cubic equations; low-order problem; nonlinear equations; nonlinear maximum likelihood estimation; normal equations; polynomial coefficients; quadratic equations; Classification algorithms; Contracts; Ear; Maximum likelihood estimation; Nonlinear equations; Parameter estimation; Polynomials; Probability; Statistics;
Conference_Titel :
Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-6405-3
DOI :
10.1109/ACSSC.1994.471596
