Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning parameter that balances the trade-off between model fitting and sparsity. In this work we introduce a new tuning parameter selction criterion based on the maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to a real data on prostate cancer disease.

Sottile, G., Muggeo, V. (2019). A new tuning parameter selector in lasso regression. In G. Arbia, S. Peluso, A. Pini, G. Rivellini (a cura di), Smart Statistics for Smart Applications - Book of Short Papers SIS2019 (pp. 541-547). Milano : Pearson.

A new tuning parameter selector in lasso regression

Sottile, G
;
Muggeo, V
2019-01-01

Abstract

Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning parameter that balances the trade-off between model fitting and sparsity. In this work we introduce a new tuning parameter selction criterion based on the maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to a real data on prostate cancer disease.
9788891915108
https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Dirigenti e istituzioni/ISTITUZIONI-HE-PDF-sis2019_V4.pdf
Sottile, G., Muggeo, V. (2019). A new tuning parameter selector in lasso regression. In G. Arbia, S. Peluso, A. Pini, G. Rivellini (a cura di), Smart Statistics for Smart Applications - Book of Short Papers SIS2019 (pp. 541-547). Milano : Pearson.
File in questo prodotto:
File Dimensione Formato  
SIS2019_Sottile_Muggeo.pdf

Solo gestori archvio

Descrizione: Articolo principale
Tipologia: Versione Editoriale
Dimensione 880.8 kB
Formato Adobe PDF
880.8 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/419564
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact