Deviations from the center within a robust neighborhood may naturally be considered an infinite dimensional nuisance parameter. Thus, in principle, the semiparametric method may be tried, which is to compute the scores function for the main parameter minus its orthogonal projection on the closed...

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...

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...

Deviations from the center within a robust neighborhood may naturally be considered an infinite dimensional nuisance parameter. Thus, in principle, the semiparametric method may be tried, which is to compute the scores function for the main parameter minus its orthogonal projection on the closed...

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,...