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

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Abstract

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 √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.

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Authors and Affiliations

  1. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China

    XiaoPing Chen, JianHua Shi & Yong Zhou

  2. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, 350117, China

    XiaoPing Chen

  3. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, 363000, China

    JianHua Shi

  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Yong Zhou

Authors
  1. XiaoPing Chen
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  2. JianHua Shi
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  3. Yong Zhou
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Correspondence to Yong Zhou.

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Chen, X., Shi, J. & Zhou, Y. Monotone rank estimation of transformation models with length-biased and right-censored data. Sci. China Math. 58, 1–14 (2015). https://doi.org/10.1007/s11425-015-5035-z

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