ABSTRACT
Michael Sørensen
Department of Mathematical Sciences, University of Copenhagen Universitetsparken 5, DK-2100 Copenhagen Ø, Denmark
1.1 Introduction
In this chapter we consider parametric inference based on discrete time observations X0, Xt1 , . . . , Xtn from a d-dimensional stochastic process. In most of the chapter the statistical model for the data will be a diffusion model given by a stochastic differential equation. We shall, however, also consider some examples of non-Markovian models, where we typically assume that the data are partial observations of a multivariate stochastic differential equation. We assume that the statistical model is indexed by a p-dimensional parameter θ.
