7 papers
Robust Instrumental Variables: Sharp Rates and Inference under Adversarial Contamination
Anders Bredahl Kock, David Preinerstorfer
Because 2SLS is built from sample averages, a small number of observations can have a disproportionate effect on estimates and inference. We introduce W-2SLS, a simple drop-in robu…
Adversarially robust multiple testing in high dimensions
Anders Bredahl Kock, David Preinerstorfer
Robust multiple testing procedures for assessing equality restrictions on the coordinates of high-dimensional mean vectors are proposed. Our procedures are based on quantile-winsor…
High-dimensional Gaussian and bootstrap approximations for robust means
Anders Bredahl Kock, David Preinerstorfer
Recent years have witnessed much progress on Gaussian and bootstrap approximations to the distribution of sums of independent random vectors with dimension large relative to th…
Winsorized mean estimation with heavy tails and adversarial contamination
Anders Bredahl Kock, David Preinerstorfer
Finite-sample upper bounds on the estimation error of a winsorized mean estimator of the population mean in the presence of heavy tails and adversarial contamination are establishe…
Robustness for free: asymptotic size and power of max-tests in high dimensions
Anders Bredahl Kock, David Preinerstorfer
Allowing for adversarial contamination and heavy tails, we study testing whether the mean of a high-dimensional random vector equals zero. Because standard max-tests based on sampl…
Regularizing Fairness in Optimal Policy Learning with Distributional Targets
Anders Bredahl Kock, David Preinerstorfer
A decision maker typically (i) incorporates training data to learn about the relative effectiveness of treatments, and (ii) chooses an implementation mechanism that implies an ``op…