Counting process-based dimension reduction methods for censored outcomes
针对右删失生存数据，提出基于计数过程的降维方法，通过半参数估计方程估计失效时间模型的降维子空间，无需估计删失分布且避免维数灾难，数值研究显示性能显著提升。
We propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate for the bias in estimating the dimension reduction subspace. Second, the nonparametric estimation involved adapts to the structural dimension, so our methods circumvent the curse of dimensionality. Asymptotic normality is established for the estimators. We propose a computationally efficient approach that requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance. The methods are implemented in the [Formula: see text] package [Formula: see text].