Censored Interquantile Regression Model with Time-Dependent Covariates
针对传统删失分位数回归估计不稳定、方差大的问题，提出一类含时变协变量的分位数间回归模型，通过整合相邻分位数信息提高估计效率，并发展了加权秩估计和扰动重抽样方法。
Conventionally, censored quantile regression stipulates a specific, pointwise conditional quantile of the survival time given covariates. Despite its model flexibility and straightforward interpretation, the pointwise formulation oftentimes yields rather unstable estimates across neighboring quantile levels with large variances. In view of this phenomenon, we propose a new class of quantile-based regression models with time-dependent covariates for censored data. The models proposed aim to capture the relationship between the failure time and the covariate processes of a target population that falls within a specific quantile bracket. The pooling of information within a homogeneous neighborhood facilitates more efficient estimates hence, more consistent conclusion on statistical significances of the variables concerned. This new formulation can also be regarded as a generalization of the accelerated failure time model for survival data in the sense that it relaxes the assumption of global homogeneity for the error at all quantile levels. By introducing a class of weighted rank-based estimation procedure, our framework allows a quantile-based inference on the covariate effect with a less restrictive set of assumptions. Numerical studies demonstrate that the proposed estimator outperforms existing alternatives under various settings in terms of smaller empirical biases and standard deviations. A perturbation-based resampling method is also developed to reconcile the asymptotic distribution of the parameter estimates. Finally, consistency and weak convergence of the proposed estimator are established via empirical process theory. Supplementary materials for this article are available online.