Accelerated failure time model with quantile information

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• 作者：Mu Zhao ; Yixin Wang ; Yong Zhou
• 关键词：AFT model ; Non ; smooth estimating equation ; Inverse probability weighted ; Generalized moment method ; Empirical likelihood
• 刊名：Annals of the Institute of Statistical Mathematics
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：68
• 期：5
• 页码：1001-1024
• 全文大小：546 KB
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• 作者单位：Mu Zhao (1)
  Yixin Wang (2)
  Yong Zhou (2) (3)
  
  1. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China
  2. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China
  3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics
  Statistics for Business, Economics, Mathematical Finance and Insurance
  
• 出版者：Springer Netherlands
• ISSN：1572-9052
• 卷排序：68

文摘

The censored linear regression model, also referred to as the accelerated failure time model, is a useful alternative to the popular Cox model in the analysis of censored survival data. In this paper, we combine the quantile information with censored least-squares normal equations to get estimators with smaller estimated standard error for regression parameters. An inverse probability-weighted method is proposed to construct unbiased estimating equations with censored data and the lack of smoothness of the objective equations is overcome by replacing them with smooth approximations. The proposed estimators are established based on the empirical likelihood method and generalized method of moments, respectively, and their asymptotic properties are studied under some regular conditions. We also conduct some simulation experiments to investigate the finite-sample properties of the proposed estimators. The Stanford Heart Transplant data are used to illustrate the proposed estimating method. Keywords AFT model Non-smooth estimating equation Inverse probability weighted Generalized moment method Empirical likelihood