On estimator efficiency in stochastic processes

Trevor Sweeting

Stochastic Processes and their Applications, 1983, vol. 15, issue 1, 93-98

Abstract: 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).

Keywords: Primary; 62M99; Secondary; 62F20; Maximum; likelihood; estimation; inference; from; stochastic; processes; limiting; probability; of; concentration (search for similar items in EconPapers)
Date: 1983
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