On estimator efficiency in stochastic processes.
Stochastic Processes and their Applications
93 - 98.
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).
|Title:||On estimator efficiency in stochastic processes|
|Keywords:||Maximum likelihood estimation, Inference from stochastic processes, Limiting probability of concentration|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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