Can I read A Polynomial Algorithm for Best-subset Selection Problem on EtoBox?

A Polynomial Algorithm for Best-subset Selection Problem by Junxian Zhu; Canhong Wen; Jin Zhu; Heping Zhang; Xueqin Wang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is A Polynomial Algorithm for Best-subset Selection Problem about?

Best-subset selection aims to find a small subset of predictors, so that the resulting linear model is expected to have the most desirable prediction accuracy. It is not only important and imperative in regression analysis but also has far-reaching applications in every facet of research, including computer science and medicine. We introduce a polynomial algorithm, which, under mild conditions, solves the problem. This algorithm exploits the idea of sequencing and splicing to reach a stable solution in finite steps when the sparsity level of the model is fixed but unknown. We define an information criterion that helps the algorithm select the true sparsity level with a high probability. We show that when the algorithm produces a stable optimal solution, that solution is the oracle estimator of the true parameters with probability one. We also demonstrate the power of the algorithm in several numerical studies.

Who reads A Polynomial Algorithm for Best-subset Selection Problem?

Author

Junxian Zhu; Canhong Wen; Jin Zhu; Heping Zhang; Xueqin Wang

Publisher

National Academy of Sciences; Proceedings of the National Academy of Sciences; Oxford University Press (OUP); Publons; ScienceOpen (ISSN 0027-8424)

Published

2020

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)