12 papers
Private Rate-Double-Robust Inference
Máté Kormos, Aad van der Vaart
We reconcile privacy protection and rate-double-robust inference. The privacy of individuals is protected by a local privacy mechanism: injecting noise into their sensitive data, r…
Efficient Bayesian Inference in Strictly Semi-parametric Linear Inverse Problems
Adel Magra, Aad van der Vaart
We consider the efficient inference of finite dimensional parameters arising in the context of inverse problems. Our setup is the observation of a transformation of an unknown infi…
Semi-parametric Bernstein-von Mises Theorem in a Parabolic PDE Problem
Adel Magra, Frank van der Meulen, Aad van der Vaart
We consider the heat equation with absorption in a bounded domain of , where both the scalar diffusivity and the absorption function are unknown. We investigate a Bay…
Bootstrapping not under the null?
Alexis Derumigny, Miltiadis Galanis, Wieger Schipper +1
We propose a bootstrap testing framework for a general class of hypothesis tests, which allows resampling under the null hypothesis as well as other forms of bootstrapping. We iden…
Semiparametric Bernstein-von Mises Phenomenon via Isotonized Posterior in Wicksell's problem
Francesco Gili, Geurt Jongbloed, Aad van der Vaart
In this paper, we propose a novel Bayesian approach for nonparametric estimation in Wicksell's problem. This has important applications in astronomy for estimating the distribution…
Misspecified Bernstein-Von Mises theorem for hierarchical models
Geerten Koers, Botond Szabó, Aad van der Vaart
We derive a Bernstein von-Mises theorem in the context of misspecified, non-i.i.d., hierarchical models parametrized by a finite-dimensional parameter of interest. We apply our res…