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- W943753712 abstract "The chapter presents a variety of maximum-likelihood methods. The primary purpose is to present the algorithms of exact maximum-likelihood estimation. A secondary purpose is to show how some of the methods of least-squares estimation may be construed as conditional maximum-likelihood methods which adopt differing approaches to the starting-value problem. It adopts a notation of sufficient generality that is also capable of showing the finer details. The exact maximum-likelihood estimates of an autoregressive process may be found by an iterative procedure using one of the algorithms for nonlinear optimization. These algorithms depend upon a facility for evaluating the criterion function and its derivatives at an arbitrary point within an admissible parameter space. The complexities of the exact maximum-likelihood estimator are due, in large measure, to the effect of the starting values. The estimation can be simplified considerably if these values are preassigned or if their estimation can be separated from that of the principal parameters of the model. This may be achieved via a conditional-likelihood approach." @default.
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- W943753712 date "1999-01-01" @default.
- W943753712 modified "2023-09-23" @default.
- W943753712 title "Maximum-Likelihood Methods of ARMA Estimation" @default.
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- W943753712 doi "https://doi.org/10.1016/b978-012560990-6/50024-5" @default.
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