Estimating functions for diffusion-type processes

TY - CHAP

T1 - Estimating functions for diffusion-type processes

AU - Sørensen, Michael

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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.

AB - 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.

M3 - Book chapter

SN - 978-1-4398-4940-8

T3 - Monographs on Statistics and Applied Probability

SP - 1

EP - 107

BT - Statistical Methods for Stochastic Differential Equations

A2 - Kessler, Mathieu

A2 - null, Alexander Lindner

A2 - Sørensen, Michael

PB - CRC Press

ER -