6533b827fe1ef96bd128619f
RESEARCH PRODUCT
A new tuning parameter selector in lasso regression
G SottileV Muggeosubject
Least absolute shrinkage and selection operator (lasso) Model selection Variable selection Penalized likelihood Signal-to-noise ratio Clinical datadescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |