Scalable Collaborative Targeted Learning for High-Dimensional Data - Université Paris Descartes (Paris 5) Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2017

Scalable Collaborative Targeted Learning for High-Dimensional Data

Résumé

Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation (C-TMLE) procedure. The original implementation/instantiation of the C-TMLE template can be presented as a greedy forward stepwise C-TMLE algorithm. It does not scale well when the number p of covariates increases drastically. This motivates the introduction of a novel implementation/instantiation of the C-TMLE template where the covariates are pre-ordered. Its time complexity is O(p) as opposed to the original O(p 2), a remarkable gain. We propose two pre-ordering strategies and suggest a rule of thumb to develop other meaningful strategies. Because it is usually unclear a priori which pre-ordering strategy to choose, we also introduce another implementation/instantiation called SL-C-TMLE algorithm that enables the data-driven choice of the better pre-ordering strategy given the problem at hand. Its time complexity is O(p) as well. The computational burden and relative performance of these algorithms were compared in simulation studies involving fully synthetic data or partially synthetic data based on a real world large electronic health database, and in analyses of three real, large electronic health databases. In all analyses involving electronic health databases, the greedy C-TMLE algorithm is unacceptably slow. Simulation studies indicate our scalable C-TMLE and SL-C-TMLE algorithms work well. All C-TMLEs are publicly available in a Julia software package.
Fichier principal
Vignette du fichier
C-TMLE_SMMR.pdf (434.1 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01487569 , version 1 (12-03-2017)

Identifiants

  • HAL Id : hal-01487569 , version 1

Citer

Cheng J Ju, Susan Gruber, Samuel D Lendle, Antoine Chambaz, Jessica J Franklin, et al.. Scalable Collaborative Targeted Learning for High-Dimensional Data. 2017. ⟨hal-01487569⟩

Relations

256 Consultations
122 Téléchargements

Partager

Gmail Facebook X LinkedIn More