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On estimator efficiency in stochastic processes

Sweeting, TJ; (1983) On estimator efficiency in stochastic processes. Stochastic Processes and their Applications , 15 pp. 93-98. 10.1016/0304-4149(83)90023-6.

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It is shown, under mild regularity conditions on the random information matrix, that the maximum likelihood estimator is efficient in the sense of having asymptotically maximum probability of concentration about the true parameter value. In the case of a single parameter, the conditions are improvements of those used by Heyde (1978). The proof is based on the idea of maximum probability estimators introduced by Weiss and Wolfowitz (1967).

Type: Article
Title: On estimator efficiency in stochastic processes
DOI: 10.1016/0304-4149(83)90023-6
Keywords: Maximum likelihood estimation, Inference from stochastic processes, Limiting probability of concentration
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Maths and Physical Sciences
URI: http://discovery.ucl.ac.uk/id/eprint/75701
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