The conditional glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.

SFX


Data di pubblicazione: 2019
Titolo: An extension of the censored gaussian lasso estimator
Autori: 
AUGUGLIARO, Luigi (Corresponding)
Sottile, Gianluca
mostra contributor esterni 
Citazione: Luigi Augugliaro, Gianluca Sottile, & Veronica Vinciotti (2019). An extension of the censored gaussian lasso estimator. In Smart Statistics for Smart Applications - Book of Short Papers SIS2019 (pp. 39-46). Milano : Arbia, G; Peluso, S; Pini, A; Rivellini, G.
Abstract: The conditional glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.
ISBN: 9788891915108
Settore Scientifico Disciplinare: Settore SECS-S/01 - Statistica
Appare nelle tipologie:2.07 Contributo in atti di convegno pubblicato in volume

File in questo prodotto:
FileDescrizioneTipologiaLicenza 
AugugliaroEtAl_SIS19.pdf Articolo principaleVersione EditorialeOpen Access Visualizza/Apri
Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/10447/418527
  • Citazioni
  • ND
  • ND
  • ND

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
 
 
 
Powered by IRIS-about IRIS-Utilizzo dei cookie
Logo CINECA  Copyright © 2020