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Abstract:
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In an ultra-high dimensional setting with a huge number of covariates, variable screening is useful for dimension reduction before a more refined variable selection and parameter estimation method is applied. In this talk I will present a new sure joint screening procedure for right-censored time-to-event data based on a sparsity-restricted semiparametric accelerated failure time model. Our method, referred to as Buckley-James assisted sure screening (BJASS), consists of an initial screening step using a sparsity-restricted least-squares estimate based on a synthetic time variable and a refinement screening step using a sparsity-restricted least-squares estimate with the Buckley-James imputed event times. The refinement step may be repeated several times to obtain more stable results. We show that with any fixed number of refinement steps, the BJASS procedure retains all important variables with probability tending to 1. We illustrate its performance in comparison with some marginal screening methods using both simulated and real data examples. We have implemented the BJASS method using Matlab and made it available through Github https://github.com/yiucla/BJASS.
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