一种生存数据非线性充分降维子空间估计的新方法

摘要

给出了一种估计生存数据非线性充分降维子空间的新方法。利用再生核Hilbert空间性质以及双切片思想，建立广义特征谱分解问题与获得充分降维子空间的联系，以此估计生存时间和删失时间的联合非线性降维中心子空间。进一步结合SDR中心子空间的性质，通过联合SDR中心子空间来估计权重函数，在算法实现过程中，利用迭代思想，达到提高估计效率的目的。最后通过数值模拟来说明该方法的优良性。%An approach was proposed to estimating the nonlinear sufficient dimension reduction (SDR) subspace for survival data with censorship .Based on the theory of reproducing kernel Hilbert spaces (RK HS ) and the double slicing procedure ,the joint nonlinear sufficient dimension reduction central subspace was estimated by means of the generalized eigen‐decomposition equation . And the weight function was estimated by the definition and property of SDR central subspace . The efficiency was improved by the iteration method while the algorithm was being implemented .Finally ,the performance of the proposed method was illustrated on simulated data .