Estimating parameters in diffusion processes using an approximate maximum likelihood approach
✍ Scribed by Erik Lindström
- Publisher
- Springer US
- Year
- 2006
- Tongue
- English
- Weight
- 453 KB
- Volume
- 151
- Category
- Article
- ISSN
- 0254-5330
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
To obtain the likelihood of a non-Gaussian state-space model, Durbin and Koopman (1997, Biometrika, 84, 669 -684) ÿrst calculate the likelihood under an approximating linear Gaussian model and then use Monte Carlo methods to estimate the necessary adjustment factor. We show that Durbin and Koopman's
## Berry-Esseen bounds, with random and nonrandom normings, and large deviation probability bounds for two approximate maximum likelihood estimators of the drift parameter in the Ornstein-Uhlenbeck process are obtained when the process is observed at equally spaced dense time points. Also obtained
In this paper we apply compactly supported wavelets to the ARFIMA(p, d, q) longmemory process to develop an alternative maximum likelihood estimator of the di!erencing parameter, d, that is invariant to unknown means, model speci"cation, and contamination. We show that this class of time series have