Differentially Private Methods for Managing Model Uncertainty in Linear Regression
Abstract
In this article, we propose differentially private methods for hypothesis testing, model averaging, and model selection for normal linear models. We propose 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.
Cite
Text
Peña and Barrientos. "Differentially Private Methods for Managing Model Uncertainty in Linear Regression." Journal of Machine Learning Research, 2024.Markdown
[Peña and Barrientos. "Differentially Private Methods for Managing Model Uncertainty in Linear Regression." Journal of Machine Learning Research, 2024.](https://mlanthology.org/jmlr/2024/pena2024jmlr-differentially/)BibTeX
@article{pena2024jmlr-differentially,
title = {{Differentially Private Methods for Managing Model Uncertainty in Linear Regression}},
author = {Peña, Víctor and Barrientos, Andrés F.},
journal = {Journal of Machine Learning Research},
year = {2024},
pages = {1-44},
volume = {25},
url = {https://mlanthology.org/jmlr/2024/pena2024jmlr-differentially/}
}