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Activity Number:
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378
- LiDS Student Paper Award Winners: Topic-Contributed Papers
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Lifetime Data Science Section
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Abstract #322527
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Title:
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Optimal Subsampling for Parametric Accelerated Failure Time Models with Massive Survival Data
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Author(s):
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Zehan Yang* and Jun Yan and HaiYing Wang
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Companies:
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University of Connecticut and University of Connecticut and Uninversity of Connecticut
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Keywords:
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A-optimality;
Survival analysis
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Abstract:
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With increasing availability of massive survival data, researchers need valid statistical inferences for survival modeling whose computation is not limited by computer memories. Existing works focus on relative risk models using the online updating and divide-and-conquer strategies. The subsampling strategy has not been available due to challenges in developing the asymptotic properties of the estimator under semiparametric models with censored data. This paper tackles optimal subsampling algorithms to fast approximate the maximum likelihood estimator for parametric accelerate failure time (AFT) models with massive survival data. We derive the asymptotic distributions of the subsampling estimator and the optimal sampling probabilities that minimize the asymptotic mean squared error of the estimator. A feasible two-step algorithm is proposed where the optimal sampling probabilities in the second step are estimated based on a pilot sample in the first step. The asymptotic properties of the two-step estimator are established. The performance of the estimator is validated in a simulation study. A real data analysis illustrates the
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Authors who are presenting talks have a * after their name.
