Right-censored models on massive data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915132526821376 |
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| author | Ciuperca, Gabriela |
| author_facet | Ciuperca, Gabriela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Right-censored models on massive data Ciuperca, Gabriela Statistics Theory 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. |
| title | Right-censored models on massive data |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2502.00178 |