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Hypothesis testing with e-values
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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.
Forward citations
Cited by 5 Pith papers
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E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing
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Hypothesis Testing in Imaging Inverse Problems
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Hypothesis testing for community structure in temporal networks using e-values
A temporal-network community test averages e-values obtained from per-snapshot p-values, retaining valid type I error control under arbitrary dependence.
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