10 papers
Bentkus-type asymptotic e-values
Diego Martinez-Taboada, Ben Chugg, Aaditya Ramdas
Asymptotic e-values are emerging as a powerful alternative to asymptotic p-values, particularly in post-hoc inference and multiple testing, where significance levels may be data-de…
E-values as statistical evidence: A comparison to Bayes factors, likelihoods, and p-values
Ben Chugg, Aaditya Ramdas, Peter Grünwald
A recurring debate in the philosophy of statistics concerns what, exactly, should count as a measure of evidence for or against a given hypothesis. P-values, likelihood ratios, and…
Post-Hoc Large-Sample Statistical Inference
Ben Chugg, Etienne Gauthier, Michael I. Jordan +2
We derive inferential procedures for large sample sizes that remain valid under data-dependent significance levels (so-called "post-hoc valid inference"). Classical statistical too…
A variational approach to dimension-free self-normalized concentration
Ben Chugg, Aaditya Ramdas
We study the self-normalized concentration of vector-valued stochastic processes. We focus on bounds for "sub-" processes, a well-known and quite general class of process that…
On admissibility in post-hoc hypothesis testing
Ben Chugg, Tyron Lardy, Aaditya Ramdas +1
The validity of classical hypothesis testing requires the significance level be fixed before any statistical analysis takes place. This is a stringent requirement. For instanc…
Closed-form empirical Bernstein confidence sequences for scalars and matrices
Ben Chugg, Aaditya Ramdas
We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields…