Monotone rank estimation of transformation models with length-biased and right-censored data

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• 作者：XiaoPing Chen ; JianHua Shi ; Yong Zhou
• 关键词：monotone rank estimation ; length ; biased data ; right ; censored data ; random weighting ; transformation model ; 62F10 ; 62F12 ; 62F40
• 刊名：SCIENCE CHINA Mathematics
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：58
• 期：10
• 页码：1-14
• 全文大小：215 KB
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• 作者单位：XiaoPing Chen (1) (2)
  JianHua Shi (1) (3)
  Yong Zhou (1) (4)
  
  1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China
  2. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350117, China
  3. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, 363000, China
  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Chinese Library of Science
  Applications of Mathematics
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1862

文摘

This paper considers the monotonic transformation model with an unspecified transformation function and an unknown error function, and gives its monotone rank estimation with length-biased and rightcensored data. The estimator is shown to be ∥m class="EmphasisTypeItalic ">n -consistent and asymptotically normal. Numerical simulation studies reveal good finite sample performance and the estimator is illustrated with the Oscar data set. The variance can be estimated by a resampling method via perturbing the U-statistics objective function repeatedly. Keywords monotone rank estimation length-biased data right-censored data random weighting transformation model