On the sign consistency of the Lasso for the high-dimensional Cox model

DOI10.1016/j.jmva.2018.04.005zbMath1395.62309OpenAlexW2799962551MaRDI QIDQ1661333

Heng Lian, Mengying You, Jian Huang, Huazhen Lin, Shao-Gao Lv

Publication date: 16 August 2018

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2018.04.005

zbMATH Keywords

empirical processoracle propertyLassosparse recoverymutual coherencehazard modelCox proportional

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Estimation in survival analysis and censored data (62N02)

Related Items

Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signal Detection, A penalized estimation for the Cox model with ordinal multinomial covariates

Uses Software

• PDCO

Cites Work

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• A unified approach to model selection and sparse recovery using regularized least squares
• Oracle inequalities for the lasso in the Cox model
• Regularization for Cox's proportional hazards model with NP-dimensionality
• Probability in Banach spaces. Isoperimetry and processes
• Cox's regression model for counting processes: A large sample study
• Support recovery without incoherence: a case for nonconvex regularization
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• A general class of exponential inequalities for martingales and ratios
• Weak convergence and empirical processes. With applications to statistics
• Introduction to empirical processes and semiparametric inference
• High-dimensional graphs and variable selection with the Lasso
• Atomic Decomposition by Basis Pursuit
• Asymptotic Statistics
• Testing and Confidence Intervals for High Dimensional Proportional Hazards Models
• High-Dimensional Sparse Additive Hazards Regression
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Probability Inequalities for Sums of Bounded Random Variables
• Non-asymptotic oracle inequalities for the high-dimensional cox regression via lasso
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• Compressed sensing