This book is written to offer a humble, but unified, treatment of e-values in hypothesis testing.
The book is organized into three parts: Fundamental Concepts, Core Ideas, and Advanced …

The task of distinguishing individuals of interest from a vast pool of candidates using
predictive models has garnered significant attention in recent years. This task can be framed …

We explicitly define the notion of (exact or approximate) compound e-values which have
been implicitly presented and extensively used in the recent multiple testing literature. We …

The rejection threshold used for e-values and e-processes is by default set to $1/\alpha $ for
a guaranteed type-I error control at $\alpha $, based on Markov's and Ville's inequalities …

As the volume and complexity of data continue to expand across various scientific
disciplines, the need for robust methods to account for the multiplicity of comparisons has …

We prove that the only admissible way of merging e-values is to use a weighted arithmetic
average. This result completes the picture of merging methods for e-values, and generalizes …

Distribution-free predictive inference beyond the construction of prediction sets has gained a
lot of interest in recent applications. One such application is the selection task, where the …

We introduce a powerful and non-random methodology for novelty detection, offering
distribution-free false discovery rate (FDR) control guarantees. Building on the full-conformal …

Consider a scenario where a large number of explanatory features targeting a response
variable are analyzed, such that these features are partitioned into different groups …