1 Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark3 unknown4 Department of Applied Mathematics and Computer Science, Technical University of Denmark
In this paper a method is formulated in an estimating function setting for parameter estimation, which allows the use of prior information. The main idea is to use prior knowledge of the parameters, either specified as moments restrictions or as a distribution, and use it in the construction of an estimating function. It may be useful when the full Bayesian analysis is difficult to carry out for computational reasons. This is almost always the case for diffusions, which is the focus of this paper, though the method applies in other settings.
Diffusion Process; Small sample size; Ornstein-Uhlenbeck Process; Estimating Functions; Cox Ingersoll & Ross (CIR) Process