Conditional inferential models: combining information for prior-free probabilistic inference

TL;DR

This paper introduces a conditioning approach within the inferential model framework to improve prior-free probabilistic inference, especially in high-dimensional auxiliary variable scenarios, by reducing dimension and aggregating information.

Contribution

It develops a novel conditioning strategy for IMs that enhances efficiency and offers new insights into classical statistical concepts like sufficiency and Bayesian inference.

Findings

Conditional IMs improve inference efficiency in high-dimensional settings

A differential equation-driven method for selecting conditional associations is proposed

Examples demonstrate the effectiveness of local conditional IMs in normal models

Abstract

The inferential model (IM) framework provides valid prior-free probabilistic inference by focusing on predicting unobserved auxiliary variables. But, efficient IM-based inference can be challenging when the auxiliary variable is of higher dimension than the parameter. Here we show that features of the auxiliary variable are often fully observed and, in such cases, a simultaneous dimension reduction and information aggregation can be achieved by conditioning. This proposed conditioning strategy leads to efficient IM inference, and casts new light on Fisher's notions of sufficiency, conditioning, and also Bayesian inference. A differential equation-driven selection of a conditional association is developed, and validity of the conditional IM is proved under some conditions. For problems that do not admit a valid conditional IM of the standard form, we propose a more flexible class of conditional IMs based on localization. Examples of local conditional IMs in a bivariate normal model and a normal variance components model are also given.