Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models-Reference-Cited by-同舟云学术

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Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models

Published:2023 Issue: Volume:71 Page:2251-2266
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Gohain Prakash Borpatra¹^ORCID,Jansson Magnus¹^ORCID

Affiliation:

1. Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/10040758/10146465.pdf?arnumber=10146465

Reference41 articles.

1. Extended Bayesian information criteria for model selection with large model spaces

2. The sparsity and bias of the Lasso selection in high-dimensional linear regression

3. Bayesian Model Comparison With the g-Prior

4. High-dimensional graphs and variable selection with the Lasso

5. Exponentially embedded families-new approaches to model order estimation

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