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Maximum likelihood estimation for continuous-time stochastic processes

Published online by Cambridge University Press:  01 July 2016

Paul David Feigin
Paul David Feigin*
Affiliation:
Australian National University
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Abstract

This paper is mainly concerned with the asymptotic theory of maximum likelihood estimation for continuous-time stochastic processes. The role of martingale limit theory in this theory is developed. Some analogues of classical statistical concepts and quantities are also suggested. Various examples that illustrate parts of the theory are worked through, producing new results in some cases. The role of diffusion approximations in estimation is also explored.

Keywords

MAXIMUM LIKELIHOOD ESTIMATIONCONTINUOUS-TIME STOCHASTIC PROCESSESASYMPTOTIC THEORYMARTINGALE LIMIT THEORYDIFFUSION APPROXIMATIONS
Type
Research Article
Information
Advances in Applied Probability , Volume 8 , Issue 4 , December 1976 , pp. 712 - 736
Copyright
Copyright © Applied Probability Trust 1976 

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