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A sensitivity analysis and error bounds for the adaptive lasso

Basu, Tathagata; Einbeck, Jochen; Troffaes, Matthias

A sensitivity analysis and error bounds for the adaptive lasso Thumbnail


Authors

Tathagata Basu



Contributors

I. Irigoien
Editor

D. -J. Lee
Editor

J. Martinez-Minaya
Editor

M. X. Rodriguez-Alvarez
Editor

Abstract

Sparse regression is an efficient statistical modelling technique which is of major relevance for high dimensional problems. There are several ways of achieving sparse regression, the well-known lasso being one of them. However, lasso variable selection may not be consistent in selecting the true sparse model. Zou (2006) proposed an adaptive form of the lasso which overcomes this issue, and showed that data driven weights on the penalty term will result in a consistent variable selection procedure. Weights can be informed by a prior execution of least squares or ridge regression. Using a power parameter on the weights, we carry out a sensitivity analysis for this parameter, and derive novel error bounds for the Adaptive lasso.

Citation

Basu, T., Einbeck, J., & Troffaes, M. (2020). A sensitivity analysis and error bounds for the adaptive lasso. In I. Irigoien, D. -. Lee, J. Martinez-Minaya, & M. X. Rodriguez-Alvarez (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling (278-281)

Conference Name International Workshop on Statistical Modelling
Conference Location Bilbao
Acceptance Date Jun 2, 2020
Online Publication Date Jul 20, 2020
Publication Date 2020
Deposit Date Oct 1, 2020
Publicly Available Date Oct 8, 2020
Publisher Universidad del Pais Vasco
Pages 278-281
Book Title Proceedings of the 35th International Workshop on Statistical Modelling.
Public URL https://durham-repository.worktribe.com/output/1141665
Publisher URL https://web-argitalpena.adm.ehu.es/pdf/USPDF202673.pdf

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