A new tuning parameter selector in lasso regression

Autor:	Sottile, G, Muggeo, V
Přispěvatelé:	Arbia, G, Peluso, S, Pini, A, Rivellini, G, Sottile, G, Muggeo, V
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Least absolute shrinkage and selection operator (lasso) Model selection Variable selection Penalized likelihood Signal-to-noise ratio Clinical data
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=od______3658::ecaf33291ff16599e2dfa24ee09eb3bc http://hdl.handle.net/10447/419564 Zobrazit plný text záznamu