Censored linear model in high dimensions. Penalised linear regression on high-dimensional data with left-censored response variable

DOI10.1007/S11749-015-0441-7zbMath1341.62218OpenAlexW2593797460MaRDI QIDQ285835

Patric Müller, Sara van de Geer

Publication date: 19 May 2016

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-015-0441-7

zbMATH Keywords

censored \(L_1\)-regularisationhigh-dimensional censored modellasso with censored datapenalised regression with censored data

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (5)

Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads ⋮ High-dimensional robust inference for censored linear models ⋮ High-Dimensional Censored Regression via the Penalized Tobit Likelihood ⋮ -Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model ⋮ Generalized M-estimators for high-dimensional Tobit I models

Uses Software

• glasso

Cites Work

• Unnamed Item
• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• The Adaptive Lasso and Its Oracle Properties
• Statistics for high-dimensional data. Methods, theory and applications.
• Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
• The Dantzig selector and sparsity oracle inequalities
• \(\ell_{1}\)-penalization for mixture regression models
• Least absolute deviations estimation for the censored regression model
• Current status regression.
• On the conditions used to prove oracle results for the Lasso
• Simultaneous analysis of Lasso and Dantzig selector
• High-dimensional generalized linear models and the lasso
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables

This page was built for publication: Censored linear model in high dimensions. Penalised linear regression on high-dimensional data with left-censored response variable