Exact filters for Newton–Raphson parameter estimation algorithms for continuous-time partially observed stochastic systems
✍ Scribed by C.D. Charalambous; J.L. Hibey
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 156 KB
- Volume
- 42
- Category
- Article
- ISSN
- 0167-6911
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✦ Synopsis
This paper presents explicit ÿnite-dimensional ÿlters for implementing Newton-Raphson (NR) parameter estimation algorithms. The models which exhibit nonlinear parameter dependence are stochastic, continuous-time and partially observed. The implementation of the NR algorithm requires evaluation of the log-likelihood gradient and the Fisher information matrix. Fisher information matrices are important in bounding the estimation error from below, via the Cramer-Rao bound. The derivations are based on relations between incomplete and complete data, likelihood, gradient and Hessian likelihood functions, which are derived using Girsanov's measure transformations.