Statistical methods for confidential data are in high demand due to an increase in computational power and changes in privacy law. This article introduces differentially private methods for handling model uncertainty in linear regression models. More precisely, we provide differentially private Bayes factors, posterior probabilities, likelihood ratio statistics, information criteria, and model-averaged estimates. Our methods are asymptotically consistent and easy to run with existing implementations of non-private methods.
翻译:由于计算力的提高和隐私法的改变,对保密数据的统计方法的需求很大,这一条提出了处理线性回归模型模型不确定性的有差别的私人方法,更确切地说,我们提供了不同的私人贝亚因、后概率、概率比率统计、信息标准以及模型平均估计。我们的方法与非私人方法的现有实施无异且容易操作。