Maximum likelihood estimation for continuous-time stochastic processes

Paul David Feigin

136 citations0/0 files storedAdvances in Applied Probability

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Stochastic processes and financial applications Probability and Statistical Research Statistical Methods and Inference

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.

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References

1967On Square Integrable Martingales 1962A decomposition theorem for supermartingales

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