Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Suzuki, Joe
Institution: The University of Osaka
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4387595596