5 citations · 15 across the 19 of their papers we have counts for
24 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…
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 Modern Gauss-Markov Theorem? Really?
Benedikt M. Pötscher, David Preinerstorfer
We show that the theorems in Hansen (2021a) (the version accepted by Econometrica), except for one, are not new as they coincide with classical theorems like the good old Gauss-Mar…
Superconsistency of Tests in High Dimensions
Anders Bredahl Kock, David Preinerstorfer
To assess whether there is some signal in a big database, aggregate tests for the global null hypothesis of no effect are routinely applied in practice before more specialized anal…
Valid Heteroskedasticity Robust Testing
Benedikt M. Pötscher, David Preinerstorfer
Tests based on heteroskedasticity robust standard errors are an important technique in econometric practice. Choosing the right critical value, however, is not simple at all: conve…
Consistency of -norm based tests in high dimensions: characterization, monotonicity, domination
Anders Bredahl Kock, David Preinerstorfer
Many commonly used test statistics are based on a norm measuring the evidence against the null hypothesis. To understand how the choice of a norm affects power properties of tests…
Treatment recommendation with distributional targets
Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev
We study the problem of a decision maker who must provide the best possible treatment recommendation based on an experiment. The desirability of the outcome distribution resulting…
How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?
Benedikt M. Pötscher, David Preinerstorfer
We develop theoretical finite-sample results concerning the size of wild bootstrap-based heteroskedasticity robust tests in linear regression models. In particular, these results p…
Functional Sequential Treatment Allocation with Covariates
Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev
We consider a multi-armed bandit problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the…
How to avoid the zero-power trap in testing for correlation
David Preinerstorfer
In testing for correlation of the errors in regression models the power of tests can be very low for strongly correlated errors. This counterintuitive phenomenon has become known a…
Functional Sequential Treatment Allocation
Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev
Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best,…
Power in High-Dimensional Testing Problems
Anders Bredahl Kock, David Preinerstorfer
Fan et al. (2015) recently introduced a remarkable method for increasing asymptotic power of tests in high-dimensional testing problems. If applicable to a given test, their power…
Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing
Benedikt M. Pötscher, David Preinerstorfer
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample results on size and power of heteroskedasticity and autocorrelation robust tests…
Controlling the Size of Autocorrelation Robust Tests
Benedikt M. Pötscher, David Preinerstorfer
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. We investigate under which conditions the size of autocorrelation robust tests ca…
Uniformly valid confidence intervals post-model-selection
François Bachoc, David Preinerstorfer, Lukas Steinberger
We suggest general methods to construct asymptotically uniformly valid confidence intervals post-model-selection. The constructions are based on principles recently proposed by Ber…
Finite Sample Properties of Tests Based on Prewhitened Nonparametric Covariance Estimators
David Preinerstorfer
We analytically investigate size and power properties of a popular family of procedures for testing linear restrictions on the coefficient vector in a linear regression model with…
On the Power of Invariant Tests for Hypotheses on a Covariance Matrix
David Preinerstorfer, Benedikt M. Pötscher
The behavior of the power function of autocorrelation tests such as the Durbin-Watson test in time series regressions or the Cliff-Ord test in spatial regression models has been in…
On Size and Power of Heteroskedasticity and Autocorrelation Robust Tests
David Preinerstorfer, Benedikt M. Pötscher
Testing restrictions on regression coefficients in linear models often requires correcting the conventional F-test for potential heteroskedasticity or autocorrelation amongst the d…