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
MathematicsMartingale (probability theory)Maximum likelihoodApplied mathematicsEstimationLimit (mathematics)Asymptotic analysisStochastic processDiffusion processProbability theoryMathematical economicsMathematical optimizationStatisticsMathematical analysisComputer scienceInnovation diffusion
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Publication Info
- Year
- 1976
- Type
- article
- Volume
- 8
- Issue
- 04
- Pages
- 712-736
- Citations
- 116
- Access
- Closed
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Cite This
Paul David Feigin
(1976).
Maximum likelihood estimation for continuous-time stochastic processes.
Advances in Applied Probability
, 8
(04)
, 712-736.
https://doi.org/10.1017/s0001867800042890
Identifiers
- DOI
- 10.1017/s0001867800042890