Annales de l'Institut Henri Poincaré, Probabilités et Statistiques

Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model

Sarah Lemler

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In a general counting process setting, we consider the problem of obtaining a prognostic on the survival time adjusted on covariates in high-dimension. Towards this end, we construct an estimator of the whole conditional intensity. We estimate it by the best Cox proportional hazards model given two dictionaries of functions. The first dictionary is used to construct an approximation of the logarithm of the baseline hazard function and the second to approximate the relative risk. We introduce a new data-driven weighted Lasso procedure to estimate the unknown parameters of the best Cox model approximating the intensity. We provide non-asymptotic oracle inequalities for our procedure in terms of an appropriate empirical Kullback divergence. Our results rely on an empirical Bernstein’s inequality for martingales with jumps and properties of modified self-concordant functions.


Dans le cadre général d’un processus de comptage, nous intéressons à la façon d’obtenir un pronostic sur la durée de survie en fonction des covariables en grande dimension. Pour ce faire, nous construisons un estimateur de l’intensité conditionnelle. Nous l’estimons par le meilleur modèle de Cox étant donné deux dictionnaires de fonctions. Le premier dictionnaire est utilisé pour construire le logarithme du risque de base et le second, pour approximer le risque relatif. Nous introduisons une nouvelle procédure Lasso pondéré avec une pondération basée sur les données pour estimer les paramètres inconnus du meilleur modèle de Cox approximant l’intensité. Nous établissons une inégalité oracle non-asymptotique en divergence de Kullback empirique, qui est la fonction de perte la plus appropriée à notre procédure. Nos résultats reposent sur une inégalité de Bernstein pour les martingales à sauts et sur des propriétés des fonctions self-concordantes.

Article information

Ann. Inst. H. Poincaré Probab. Statist., Volume 52, Number 2 (2016), 981-1008.

Received: 12 October 2013
Revised: 11 June 2014
Accepted: 30 October 2014
First available in Project Euclid: 4 May 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62N02: Estimation 62G05: Estimation 62G08: Nonparametric regression 60E15: Inequalities; stochastic orderings

Survival analysis Right-censored data Intensity Cox proportional hazards model Semiparametric model Non-parametric model High-dimensional covariates Lasso Non-asymptotic oracle inequalities Empirical Bernstein’s inequality


Lemler, Sarah. Oracle inequalities for the Lasso in the high-dimensional Aalen multiplicative intensity model. Ann. Inst. H. Poincaré Probab. Statist. 52 (2016), no. 2, 981--1008. doi:10.1214/14-AIHP662.

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