arXiv subjects

Jelle J Goeman

Publications and source records attributed to Jelle J Goeman.

3 recordsLinked to original sources

arXiv2024

Flexible control of the median of the false discovery proportion

We introduce a multiple testing procedure that controls the median of the proportion of false discoveries (FDP) in a flexible way. The procedure only requires a vector of p-values as input and is comparable to the Benjamini-Hochberg method, which controls the mean of the FDP. Our method allows freely choosing one or several values of alpha after seeing the data -- unlike Benjamini-Hochberg, which can be very liberal when alpha is chosen post hoc. We prove these claims and illustrate them with simulations. Our procedure is inspired by a popular estimator of the total number of true hypotheses. We adapt this estimator to provide simultaneously median unbiased estimators of the FDP, valid for finite samples. This simultaneity allows for the claimed flexibility. Our approach does not assume independence. The time complexity of our method is linear in the number of hypotheses, after sorting the p-values.

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arXiv2024

On the error control of invariant causal prediction

Invariant causal prediction provides a useful framework for identifying causal predictors of a response using heterogeneous data from multiple environments. One valuable property of the original invariant causal prediction method is that it guarantees no false causal discoveries with high probability. Such a guarantee, however, can be overly conservative in some applications, resulting in few or no causal discoveries. This raises a natural question: can invariant causal prediction be equipped with less conservative error guarantees and thereby extract more causal information from the data? In this paper, we address this question by focusing on two widely used and more liberal guarantees: false discovery rate control and simultaneous true discovery bounds. A key step in our approach is to reformulate invariant causal prediction as a multiple testing problem. We then adopt the e-Closure principle to obtain (simultaneous) false discovery rate control, together with new p-to-e calibrators tailored to this setting. We also derive simultaneous true discovery bounds via closed testing, which provide additional causal information without requiring extra assumptions and retain all discoveries from the original invariant causal prediction method. Through simulations and a real data application on educational attainment of teenagers in the United States, we show that these more liberal error control guarantees can improve the practical usefulness of invariant causal prediction.

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arXiv2020

Robust testing in generalized linear models by sign-flipping score contributions

Generalized linear models are often misspecified due to overdispersion, heteroscedasticity and ignored nuisance variables. Existing quasi-likelihood methods for testing in misspecified models often do not provide satisfactory type-I error rate control. We provide a novel semi-parametric test, based on sign-flipping individual score contributions. The tested parameter is allowed to be multi-dimensional and even high-dimensional. Our test is often robust against the mentioned forms of misspecification and provides better type-I error control than its competitors. When nuisance parameters are estimated, our basic test becomes conservative. We show how to take nuisance estimation into account to obtain an asymptotically exact test. Our proposed test is asymptotically equivalent to its parametric counterpart.

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