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Parameter estimation for some non-recurrent solutions of SDE
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Hans M. Dietz
Veröffentlicht/Copyright:
25. September 2009
Summary
The present paper deals with the problem of parameter estimation for nonlinear stochastic differential equations with solution tending to infinity with time. It is shown that if the trend coefficient is asymptotically linear (like that of an Ornstein-Uhlenbeck process), then the maximum likelihood and trajectory fitting estimators are consistent and asymptotically mixing normal. That is, these estimators behave similar as in the case of a non-ergodic Ornstein-Uhlenbeck process.
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Published Online: 2009-09-25
Published in Print: 2003-01-01
© 2003 Oldenbourg Wissenschaftsverlag GmbH
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Artikel in diesem Heft
- Editorial Note
- A note on Bayesian detection of change-points with an expected miss criterion
- The estimation problem of minimum mean squared error
- Parameter estimation for some non-recurrent solutions of SDE
- Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models
- A robust generalized Bayes estimator improving on the James-Stein estimator for spherically symmetric distributions
- Tail behaviour of a general family of control charts
Artikel in diesem Heft
- Editorial Note
- A note on Bayesian detection of change-points with an expected miss criterion
- The estimation problem of minimum mean squared error
- Parameter estimation for some non-recurrent solutions of SDE
- Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models
- A robust generalized Bayes estimator improving on the James-Stein estimator for spherically symmetric distributions
- Tail behaviour of a general family of control charts
