ArticleStatistics & probability letters2025
Universally Consistent K-Sample Tests via Dependence Measures.
Article in Statistics & probability letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models.PloS one · 2025Article
- When no answer is better than a wrong answer: A causal perspective on batch effects.Imaging neuroscience (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent methods to test distributional differences. In this paper, we demonstrate the existence of a transformation that allows K-sample testing to be carried out using any dependence measure. Consequently, universally consistent K-sample testing can be achieved using a universally consistent dependence measure, such as distance correlation and the Hilbert-Schmidt independence criterion. This enables a wide range of dependence measures to be easily applied to K-sample testing.
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Registered trials
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