Work

Stability via resampling: statistical problems beyond the real line

Year: 2026

Type: article

Abstract: Model averaging techniques based on resampling methods (such as bootstrapping or subsampling) have been utilized across many areas of statistics, often with the explicit goal of promoting stability in the resulting output. We provide a finite-sample theoretical result guaranteeing the stability of bagging when applied to algorithms that return outputs that are not necessarily real-valued—for example, an algorithm that estimates a vector of weights or a density function. We empirically assess the stability of bagging on synthetic and real-world data for a range of problem settings, including causal inference, nonparametric regression, and Bayesian model selection.

Source: Electronic Journal of Statistics

DOI: 10.1214/26-ejs2576

Authors: Jake A. Soloff, Rina Foygel Barber, Rebecca Willett

Institutions: University of Michigan, University of Chicago

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $675

Funders: National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research