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arXiv:2410.23614math.ST

Hypothesis testing with e-values

Aaditya Ramdas, Ruodu Wang

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Abstract

This book is written to offer a humble, but unified, treatment of e-values in hypothesis testing. It is organized into three parts: Fundamental Concepts, Core Ideas, and Advanced Topics. The first part includes four chapters that introduce the basic concepts. The second part includes five chapters of core ideas such as universal inference, log-optimality, e-processes, operations on e-values, and e-values in multiple testing. The third part contains seven chapters of advanced topics. The book collates important results from a variety of modern papers on e-values and related concepts, and also contains many results not published elsewhere. It offers a coherent and comprehensive picture on a fast-growing research area, and is ready to use as the basis of a graduate course in statistics and related fields.

arXiv comments
Published in: Foundations and Trends in Statistics, Vol. 1: No. 1-2, pp 1-390
Journal reference
DOI
doi:10.1561/3600000002
Subjects
math.STStatistics Theory stat.MEMethodology (Statistics)

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jan 2025 to Jan 2025. Found in 1 HN submission from Dec 2025 to Dec 2025. Open submission discussions.

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    • If thresholding of P-values is the issue, E-values -- a recent, much more elegant, easier to work with, and more robust alternative to P-values -- solve this. https://arxiv.org/abs/2312.08040 https://arxiv.org/abs/2205.00901 https://arxiv.org/abs/2210.01948…

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