Lv Xiaofeng, Zhang Gupeng, Ren Guangyu
School of International Business, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
School of Public Policy and Management, University of Chinese Academy of Science, Beijing, 100049, China.
Lifetime Data Anal. 2017 Apr;23(2):275-304. doi: 10.1007/s10985-016-9357-0. Epub 2016 Jan 25.
Lifetime data is often right-censored. Recent literature on the Gini index estimation with censored data focuses on independent censoring. However, the censoring mechanism is likely to be dependent censoring in practice. This paper proposes two estimators of the Gini index under independent censoring and covariate-dependent censoring, respectively. The proposed estimators are consistent and asymptotically normal. We also evaluate the performance of our estimators in finite samples through Monte Carlo simulations. Finally, the proposed methods are applied to real data.
生存数据通常是右删失的。近期关于删失数据的基尼指数估计的文献主要集中在独立删失方面。然而，在实际中删失机制很可能是相依删失。本文分别提出了在独立删失和协变量相依删失情况下基尼指数的两个估计量。所提出的估计量是相合的且渐近正态。我们还通过蒙特卡罗模拟评估了我们的估计量在有限样本中的表现。最后，将所提出的方法应用于实际数据。
