When the model is not known and parameter testing or interval estimation is conducted after model selection, it is necessary to consider selective inference. This paper discusses this issue in the context of sparse estimation. Firstly, we describe selective inference related to Lasso as per \cite{lee}, and then present polyhedra and truncated distributions when applying it to methods such as Forward Stepwise and LARS. Lastly, we discuss the Significance Test for Lasso by \cite{significant} and the Spacing Test for LARS by \cite{ryan_exact}. This paper serves as a review article. Keywords: post-selective inference, polyhedron, LARS, lasso, forward stepwise, significance test, spacing test.

翻译：当模型未知且在模型选择后进行参数检验或区间估计时，需要考虑选择性推断问题。本文在稀疏估计的背景下探讨该问题。首先，我们依据\cite{lee}描述与Lasso相关的选择性推断，接着阐述将其应用于前向逐步回归和LARS等方法时所涉及的多面体与截断分布。最后，我们讨论\cite{significant}提出的Lasso显著性检验以及\cite{ryan_exact}提出的LARS间距检验。本文属于综述性文章。关键词：选择后推断、多面体、LARS、lasso、前向逐步回归、显著性检验、间距检验。