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

稀疏和非稀疏调整模型选择估计器的诚实置信集

• 批准号: 193726641
• 负责人: Professorin Dr. Ulrike Schneider
• 金额: --
• 依托单位: Forschungsgruppe Ökonometrie und Systemtheorie (EOS)
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/193726641?language=en
• 关键词: Honest; Confidence; Sets; Sparsely; Non

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.

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。