Valid and efficient imprecise-probabilistic inference with partial priors, II. General framework

TL;DR

This paper introduces a new, likelihood-driven framework for statistical inference that handles partial prior information and imprecise models, providing valid, efficient results with error control, bridging Bayesian and frequentist methods.

Contribution

It develops a general inferential model using outer consonant approximation, enabling valid probabilistic reasoning with partial priors and imprecise probabilities, extending inference beyond traditional Bayesian and frequentist approaches.

Findings

Provides a likelihood-driven framework compatible with Bayesian and frequentist extremes.

Enables valid inference with partial prior information and imprecise models.

Offers a data- and prior-dependent possibility measure for inference and prediction.

Abstract

Bayesian inference requires specification of a single, precise prior distribution, whereas frequentist inference only accommodates a vacuous prior. Since virtually every real-world application falls somewhere in between these two extremes, a new approach is needed. This series of papers develops a new framework that provides valid and efficient statistical inference, prediction, etc., while accommodating partial prior information and imprecisely-specified models more generally. This paper fleshes out a general inferential model construction that not only yields tests, confidence intervals, etc.~with desirable error rate control guarantees, but also facilitates valid probabilistic reasoning with de~Finetti-style no-sure-loss guarantees. The key technical novelty here is a so-called outer consonant approximation of a general imprecise probability which returns a data- and partial prior-dependent possibility measure to be used for inference and prediction. Despite some potentially unfamiliar imprecise-probabilistic concepts in the development, the result is an intuitive, likelihood-driven framework that will, as expected, agree with the familiar Bayesian and frequentist solutions in the respective extreme cases. More importantly, the proposed framework accommodates partial prior information where available and, therefore, leads to new solutions that were previously out of reach for both Bayesians and frequentists. Details are presented here for a wide range of examples, with more practical details to come in later installments.