McWhorter, L. Todd, authorScharf, Louis L., authorIEEE, publisher2007-01-032007-01-031995McWhorter, L. Todd and Louis L. Scharf, Nonlinear Maximum Likelihood Estimation of Autoregressive Time Series, IEEE Transactions on Signal Processing 43, no. 12 (December 1995): 2909-2919.http://hdl.handle.net/10217/743In 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.born digitalarticleseng©1995 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.nonlinear equationspolynomialstime seriesautoregressive processesiterative methodssignal processingmaximum likelihood estimationGaussian processesText