Right-censored models on massive data

TL;DR

This paper introduces four aggregated censored adaptive LASSO estimators for right-censored models on large datasets, demonstrating their theoretical properties and computational efficiency through simulations.

Contribution

The paper proposes novel aggregated censored adaptive LASSO estimators that maintain oracle properties and reduce computation time on massive censored datasets.

Findings

Estimators have oracle properties similar to full data methods.

Simulation results confirm theoretical properties.

Calculation time is reduced compared to full dataset methods.

Abstract

This article considers the automatic selection problem of the relevant explanatory variables in a right-censored model on a massive database. We propose and study four aggregated censored adaptive LASSO estimators constructed by dividing the observations in such a way as to keep the consistency of the estimator of the survival curve. We show that these estimators have the same theoretical oracle properties as the one built on the full database. Moreover, by Monte Carlo simulations we obtain that their calculation time is smaller than that of the full database. The simulations confirm also the theoretical properties. For optimal tuning parameter selection, we propose a BIC-type criterion.