Abstract
Although high dimensionality and heavy censoring may cause difficulties for model selection, many literatures concern the accelerated failure time (AFT) model. We perform variable selection and statistical inference for high-dimensional censoring data based on the AFT model by directly controlling the false discovery rate. We also perform some numerical simulations to evaluate the performance of the Semi-Penalized Inference with Direct False Discovery Rate Control (SPIDR) procedure for censoring data. The results show that the SPIDR is a good alternative for statistical inference of high-dimensional censoring data.
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