10 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…
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…
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…
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…
Enhanced power enhancements for testing many moment equalities: Beyond the - and -norm
Anders Bredahl Kock, David Preinerstorfer
Contemporary testing problems in statistics are increasingly complex, i.e., high-dimensional. Tests based on the - and -norm have received considerable attention in such…
Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions
Anders Bredahl Kock, Rasmus Søndergaard Pedersen, Jesper Riis-Vestergaard Sørensen
Lasso-type estimators are routinely used to estimate high-dimensional time series models. The theoretical guarantees established for these estimators typically require the penalty…
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…
A remark on moment-dependent phase transitions in high-dimensional Gaussian approximations
Anders Bredahl Kock, David Preinerstorfer
In this article, we study the critical growth rates of dimension below which Gaussian critical values can be used for hypothesis testing but beyond which they cannot. We are partic…
A Ridge-Regularised Jackknifed Anderson-Rubin Test
Max-Sebastian Dovì, Anders Bredahl Kock, Sophocles Mavroeidis
We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments -- possibly more than the number of observatio…