In this paper we consider the problem of inference on a class of sets describing a collection of admissible models as … inference procedures are also more powerful than the structured projection methods, which rely upon building confidence sets for …

We consider the problem of inference on a class of sets describing a collection of admissible models as solutions to a … parameters, making them especially appealing in the target applications. We show that the resulting inference procedures are more …

In this paper we introduce various set inference problems as they appear in finance and propose practical and powerful … discount factors and the admissible mean-variance sets of asset portfolios. We propose to make inference on such sets using … inference on sets. …

In this paper we consider the problem of inference on a class of sets describing a collection of admissible models as … especially appealing in the target applications. Moreover, the resulting inference procedures are also more powerful than the …

We consider the problem of inference on a class of sets describing a collection of admissible models as solutions to a … parameters, making them especially appealing in the target applications. We show that the resulting inference procedures are more …

In this paper we consider the problem of inference on a class of sets describing a collection of admissible models as … especially appealing in the target applications. Moreover, the resulting inference procedures are also more powerful than the …

In this paper we introduce various set inference problems as they appear in finance and propose practical and powerful … discount factors and the admissible mean-variance sets of asset portfolios. We propose to make inference on such sets using … inference on sets. …

treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced … methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are … uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of …