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<p>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 computation time is less than that of the full database. The simulations confirm also the theoretical properties. For optimal tuning parameter selection, we propose a BIC-type criterion. Moreover, an algorithm for computationally implementing of the proposed estimators on the real data is presented. Thus, our method is applied to tooth survival related to various predictive variables.</p>

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Right-censored models on massive data

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Gabriela Ciuperca

摘要

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 computation time is less than that of the full database. The simulations confirm also the theoretical properties. For optimal tuning parameter selection, we propose a BIC-type criterion. Moreover, an algorithm for computationally implementing of the proposed estimators on the real data is presented. Thus, our method is applied to tooth survival related to various predictive variables.