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Akademický článek
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We provide a general condition under which e-variables in the form of a simple-vs.-simple likelihood ratio exist when the null hypothesis is a composite, multivariate exponential family. Such `simple' e-variables are easy to compute and expected-log-
Externí odkaz:
http://arxiv.org/abs/2404.19465
We develop anytime-valid tests of invariance under the action of compact groups. The resulting test statistics are optimal in a logarithmic-growth sense. We apply our method to extend recent anytime-valid tests of independence and to construct tests
Externí odkaz:
http://arxiv.org/abs/2401.15461
Information projections have found important applications in probability theory, statistics, and related areas. In the field of hypothesis testing in particular, the reverse information projection (RIPr) has recently been shown to lead to growth-rate
Externí odkaz:
http://arxiv.org/abs/2306.16646
Autor:
Jean-Baptiste Ducloyer, Alexandra Poinas, Léa Duchesne, Pascal Caillet, Corinne Lejus-Bourdeau, Guylène Le Meur, Michel Weber, Catherine Ivan, Nadège Limousin, Thomas Desmidt, Patrick Pladys, Pierre-Jean Pisella, Anne Bernard, Hubert Lardy, Philippe Gohier, Ludovic Martin, Frédéric Mouriaux, Pierre Lebranchu, Raoul Kanav Khanna, E3CAPS group
Publikováno v:
BMC Medical Education, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Introduction Training novice ophthalmology residents on the EyeSi® simulator increases cataract surgery safety. However, there is no consensus regarding how much training residents should perform before their first time on patients. We eval
Externí odkaz:
https://doaj.org/article/394aab4aa07f4761b5f5bad2443d9060
We develop and compare e-variables for testing whether $k$ samples of data are drawn from the same distribution, the alternative being that they come from different elements of an exponential family. We consider the GRO (growth-rate optimal) e-variab
Externí odkaz:
http://arxiv.org/abs/2303.00471
We propose a sequential, anytime-valid method to test the conditional independence of a response $Y$ and a predictor $X$ given a random vector $Z$. The proposed test is based on e-statistics and test martingales, which generalize likelihood ratios an
Externí odkaz:
http://arxiv.org/abs/2209.12637
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 group G on the data, there exists a maximally invariant statistic. We
Externí odkaz:
http://arxiv.org/abs/2208.07610