Regularized e-processes: anytime valid inference with knowledge-based efficiency gains

Classical statistical methods have theoretical justification when the sample size is predetermined by the data-collection plan. In applications, however, it's often the case that sample sizes aren't predetermined; instead, investigators might use the data observed along the way to make on-the-fly decisions about when to stop data collection. Since those methods designed for static sample sizes aren't reliable when sample sizes are dynamic, there's been a recent surge of interest in e-processes and the corresponding tests and confidence sets that are anytime valid in the sense that their justification holds up for arbitrary dynamic data-collection plans. But if the investigator has relevant-yet-incomplete prior information about the quantity of interest, then there's an opportunity for efficiency gain, but existing approaches can't accommodate this. Here I build a new, regularized e-process framework that features a knowledge-based, imprecise-probabilistic regularization that offers improved efficiency. A generalized version of Ville's inequality is established, ensuring that inference based on the regularized e-process remains anytime valid in a novel, knowledge-dependent sense. In addition to anytime valid hypothesis tests and confidence sets, the proposed regularized e-processes facilitate possibility-theoretic uncertainty quantification with strong frequentist-like calibration properties and other Bayesian-like features: satisfies the likelihood principle, avoids sure-loss, and offers formal decision-making with reliability guarantees.