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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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Bibliografische gegevens
Gepubliceerd in:Stat Med
Hoofdauteurs: Zuo, Lulu, Zhang, Haixiang, Wang, HaiYing, Liu, Lei
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: 2020
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Online toegang: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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