  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.
