High‐dimensional robust inference for Cox regression models using desparsified Lasso
针对可能设定错误的Cox比例风险模型，提出一种去稀疏Lasso估计量，证明其收敛到伪真实参数，并给出渐近正态性，从而在高维场景下实现稳健的统计推断。
Abstract We consider high‐dimensional inference for potentially misspecified Cox proportional hazard models based on low‐dimensional results by Lin and Wei (1989). A desparsified Lasso estimator is proposed based on the log partial likelihood function and shown to converge to a pseudo‐true parameter vector. Interestingly, the sparsity of the true parameter can be inferred from that of the above limiting parameter. Moreover, each component of the above (nonsparse) estimator is shown to be asymptotically normal with a variance that can be consistently estimated even under model misspecifications. In some cases, this asymptotic distribution leads to valid statistical inference procedures, whose empirical performances are illustrated through numerical examples.