Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates

DOI10.1016/j.jspi.2015.11.005zbMath1334.62160arXiv1503.00226OpenAlexW2964205277MaRDI QIDQ254202

Marie-Luce Taupin, Agathe Guilloux, Sarah Lemler

Publication date: 8 March 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.00226

zbMATH Keywords

model selectionsemiparametric modelsurvival analysisnonparametric modelright-censored dataconcentration inequalitiesconditional hazard rate functionCox's proportional hazards modelhigh-dimensional covariatesnon-asymptotic oracle inequalities

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (1)

Non-parametric Poisson regression from independent and weakly dependent observations by model selection

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Penalized projection estimators of the Aalen multiplicative intensity
• Oracle inequalities for the lasso in the Cox model
• Regularization for Cox's proportional hazards model with NP-dimensionality
• Minimax estimation of the conditional cumulative distribution function
• Nonparametric density estimation in presence of bias and censoring
• A Bernstein-type inequality for suprema of random processes with applications to model selection in non-Gaussian regression
• Adaptive Dantzig density estimation
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Lasso-type recovery of sparse representations for high-dimensional data
• Smoothing counting process intensities by means of kernel functions
• Risk bounds for model selection via penalization
• Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
• Functional aggregation for nonparametric regression.
• Asymptotics for Lasso-type estimators.
• Least angle regression. (With discussion)
• Exponential inequalities for martingales, with application to maximum likelihood estimation for counting processes
• Adaptive estimation of the conditional intensity of marker-dependent counting processes
• Minimax risks for sparse regressions: ultra-high dimensional phenomenons
• 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
• Sparsity oracle inequalities for the Lasso
• Aggregation for Gaussian regression
• High-dimensional graphs and variable selection with the Lasso
• Stable recovery of sparse overcomplete representations in the presence of noise
• The choice of a kernel function in the graduation of counting process intensities
• The Generic Chaining
• Aggregation and Sparsity Via ℓ1 Penalized Least Squares
• Sparse Density Estimation with ℓ1 Penalties
• Some Comments on C P
• Statistical models based on counting processes
• New concentration inequalities in product spaces

This page was built for publication: Adaptive estimation of the baseline hazard function in the Cox model by model selection, with high-dimensional covariates