In this paper, we derive elementary M- and optimally robust asymptotic linear (AL)-estimates for the parameters of an Ornstein–Uhlenbeck process. Simulation and estimation of the process are already well-studied, see Iacus (Simulation and inference for stochastic differential equations....

We determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false radius is used. This is done by numerical solution of the implicit equations which determine optimal robustness, for location, scale, and linear regression models,...

We determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false radius is used. This is done by numerical solution of the implicit equations which determine optimal robustness, for location, scale, and linear regression models,...

In the setup of i.i.d. observations and a real valued differentiable functional T, locally asymptotic upper bounds are derived for the power of one-sided tests (simple, versus large values of T) and for the confidence probability of lower confidence limits (for the value of T), in the case that...

In the setup of i.i.d. observations and a real valued differentiable functional T, locally asymptotic upper bounds are derived for the power of one-sided tests (simple, versus large values of T) and for the confidence probability of lower confidence limits (for the value of T), in the case that...