Recursive Maximum Likelihood Estimation of Autoregressive Processes
Date of Original Version
A new method of autoregressive parameter estimation is presented. The technique is a closer approximation to the true maximum likelihood estimator than that obtained using linear prediction techniques. The advantage of the new algorithm is mainly for short data records and/or sharply peaked spectra. Simulation results indicate that the parameter bias as well as the variance is reduced over the Yule-Walker and the forward-backward approaches of linear prediction. Also, spectral estimates exhibit more resolution and less spurious peaks. A stable all-pole filter estimate is guaranteed. The algorithm operates in a recursive model order fashion, which allows one to successively fit higher order models to the data. © 1983 IEEE
IEEE Transactions on Acoustics, Speech, and Signal Processing
Kay, Steven M.. "Recursive Maximum Likelihood Estimation of Autoregressive Processes." IEEE Transactions on Acoustics, Speech, and Signal Processing 31, 1 (1983): 56-65. doi:10.1109/TASSP.1983.1164050.