Nonlinear maximum likelihood estimation of autoregressive time series McWhorter, L. Todd ; Scharf, Louis L. "This work was supported by Bonneville Power Administration under Contract #DEBI7990BPO7346 and by the Office of Naval Research, Statistics and Probability Branch, under Contract N00014-89-J-1070." In this paper, we describe an algorithm for finding the exact, nonlinear, maximum likelihood (ML) estimators for the parameters of an autoregressive time series. We demonstrate that the ML normal equations can be written as an interdependent set of cubic and quadratic equations in the AR polynomial coefficients. We present an algorithm that algebraically solves this set of nonlinear equations for low-order problems. For high-order problems, we describe iterative algorithms for obtaining a ML solution. Colorado State University. Libraries 1994 text ; image application/pdf ECElls00009.pdf FACFECEN100398ARTI eng c1995 IEEE
Nonlinear maximum likelihood estimation of autoregressive time series
McWhorter, L. Todd ; Scharf, Louis L.
"This work was supported by Bonneville Power Administration under Contract #DEBI7990BPO7346 and by the Office of Naval Research, Statistics and Probability Branch, under Contract N00014-89-J-1070."
In this paper, we describe an algorithm for finding the exact, nonlinear, maximum likelihood (ML) estimators for the parameters of an autoregressive time series. We demonstrate that the ML normal equations can be written as an interdependent set of cubic and quadratic equations in the AR polynomial coefficients. We present an algorithm that algebraically solves this set of nonlinear equations for low-order problems. For high-order problems, we describe iterative algorithms for obtaining a ML solution.
Colorado State University. Libraries
1994
text ; image
application/pdf
ECElls00009.pdf
FACFECEN100398ARTI
eng
c1995 IEEE