Honest Confidence Sets for Sparsely and Non-Sparsely Tuned Model Selection Estimators

在这个项目中，我们想要研究收缩估计的分布特性，例如流行的Lasso估计和其他正则化方法，目的是基于这些估计导出诚实的置信集。这种估计在最近的统计文献中引起了极大的兴趣。然而，它仍然在很大程度上是未知的，如何构建有效的信心集的基础上这样的估计-一个问题，这是理论和实际利益。

In this project we want to investigate the distributional properties of shrinkage estimators, such as the popular Lasso estimator and other regularization methods, with the aim of deriving honest confidence sets based on the these estimators. This kind of estimators has seen immense interest in recent statistics literature. However, it is still largely unknown how to construct valid confidence sets based on such an estimator – a question that is both of theoretical as well as of practical interest.