# Differentially private methods for managing model uncertainty in linear regression models

arXiv: https://arxiv.org/abs/2109.03949 · HTML: https://mlsanity.com/paper/2109.03949/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Differentially private Bayesian tests](https://mlsanity.com/paper/2401.15502/) (2024)

## Abstract (arXiv)

In this work, we propose differentially private methods for hypothesis testing, model averaging, and model selection for normal linear models. We consider Bayesian methods based on mixtures of g-priors and non-Bayesian methods based on likelihood-ratio statistics and information criteria. The procedures are asymptotically consistent and straightforward to implement with existing software. We focus on practical issues such as adjusting critical values so that hypothesis tests have adequate type I error rates and quantifying the uncertainty introduced by the privacy-ensuring mechanisms.