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Sampling-Based Estimation for Massive Survival Data with Additive Hazards Model
For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample-based estimator given the full data. The optimal subsampling probabilities...
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| Pubblicato in: | Stat Med |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7775260/ https://ncbi.nlm.nih.gov/pubmed/33145780 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sim.8783 |
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