We propose a new non-convex penalty in linear regression models. The new penalty function can be considered a competitor of the LASSO, SCAD or MCP penalties, as it guarantees sparse variable selection while reducing bias for the non-null estimates. We introduce the methodology and present some comparisons among different approaches.

Daniele Cuntrera, Vito Muggeo, & Luigi Augugliaro (2022). Variable Selection with Quasi-Unbiased Estimation: the CDF Penalty. In Proceedings of the 36th International Workshop on Statistical Modelling (pp. 144-149).

Variable Selection with Quasi-Unbiased Estimation: the CDF Penalty

Daniele Cuntrera
;
Vito Muggeo;Luigi Augugliaro
2022-07

Abstract

We propose a new non-convex penalty in linear regression models. The new penalty function can be considered a competitor of the LASSO, SCAD or MCP penalties, as it guarantees sparse variable selection while reducing bias for the non-null estimates. We introduce the methodology and present some comparisons among different approaches.
978-88-5511-309-0
Daniele Cuntrera, Vito Muggeo, & Luigi Augugliaro (2022). Variable Selection with Quasi-Unbiased Estimation: the CDF Penalty. In Proceedings of the 36th International Workshop on Statistical Modelling (pp. 144-149).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/564922
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