Testing and Confidence Intervals for High Dimensional Proportional Hazards Model

@article{Fang2014TestingAC,
  title={Testing and Confidence Intervals for High Dimensional Proportional Hazards Model},
  author={Ethan X. Fang and Yang Ning and Han Liu},
  journal={arXiv: Machine Learning},
  year={2014}
}
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Without assuming model selection consistency, we prove the asymptotic normality of these test statistics, establish their semiparametric optimality. We also develop new procedures… Expand

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