46 citations · 64 across the 7 of their papers we have counts for
7 papers
Statistical reduction before the target is known: two boundary results
Rianne de Heide
Suppose that the eventual use of data is not known when the data are reduced or collected. This note considers two simple boundary cases. In a finite statistical experiment, a stat…
Where Does the Union Bound Go? Best-Arm Identification and Strong FWER Control
Rianne de Heide
In fixed-confidence best-arm identification, proofs often use a union bound across the competing arms. From a multiple-testing point of view this can look puzzling: if the best arm…
Dynamic -closure for online hypotheses with any-time-valid evidence: closure principles and projective mergers
Rianne de Heide
Many modern testing problems are sequential along two axes: new hypotheses may arrive over time, while evidence for hypotheses already under consideration continues to evolve and m…
E-Statistics, Group Invariance and Anytime Valid Testing
Muriel Felipe Pérez-Ortiz, Tyron Lardy, Rianne de Heide +1
We study worst-case-growth-rate-optimal (GROW) e-statistics for hypothesis testing between two group models. It is known that under a mild condition on the action of the underlying…
Attribution-based Explanations that Provide Recourse Cannot be Robust
Hidde Fokkema, Rianne de Heide, Tim van Erven
Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get…
Safe Testing
Peter Grünwald, Rianne de Heide, Wouter Koolen
We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in t…
Why optional stopping can be a problem for Bayesians
Rianne de Heide, Peter D. Grünwald
Recently, optional stopping has been a subject of debate in the Bayesian psychology community. Rouder (2014) argues that optional stopping is no problem for Bayesians, and even rec…