TY - JOUR

T1 - Sample Partitioning Estimation for Ergodic Diffusions: Application to Ornstein-Uhlenbeck Diffusion

AU - Ramos, Luís Pedro Carneiro

PY - 2010/1/1

Y1 - 2010/1/1

N2 - When a diffusion is ergodic its transition density converges to its invariant density, see Durrett (1998). This convergence enabled us to introduce a sample partitioning technique that gives in each sub-sample, maximum likelihood estimators. The averages of these being a natural choice as estimators. To compare our estimators with the optimal we obtained from martingale estimating functions, see Sorensen (1998), we used the Ornstein-Uhlenbeck process for which exact simulations can be carried out.

AB - When a diffusion is ergodic its transition density converges to its invariant density, see Durrett (1998). This convergence enabled us to introduce a sample partitioning technique that gives in each sub-sample, maximum likelihood estimators. The averages of these being a natural choice as estimators. To compare our estimators with the optimal we obtained from martingale estimating functions, see Sorensen (1998), we used the Ornstein-Uhlenbeck process for which exact simulations can be carried out.

KW - Ergodic Diffusions

KW - Maximum Likelihood Estimators.

KW - Transition and Invariant Densities

KW - Martingale Estimating Functions

M3 - Article

VL - 30 (1)

SP - 117

EP - 122

JO - Discussiones Mathematicae Probability and Statistics

JF - Discussiones Mathematicae Probability and Statistics

SN - 1509-9423

IS - NA

ER -