Differentially private methods for managing model uncertainty in linear regression models
Methodology
2023-08-30 v4
Abstract
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 -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.
Cite
@article{arxiv.2109.03949,
title = {Differentially private methods for managing model uncertainty in linear regression models},
author = {Víctor Peña and Andrés F. Barrientos},
journal= {arXiv preprint arXiv:2109.03949},
year = {2023}
}