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Composite Quantile Regression Estimation for Left Censored Response Longitudinal Data

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Abstract

For left censored response longitudinal data, we propose a composite quantile regression estimator (CQR) of regression parameter. Statistical properties such as consistency and asymptotic normality of CQR are studied under relaxable assumptions of correlation structure of error terms. The performance of CQR is investigated via simulation studies and a real dataset analysis.

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Authors and Affiliations

  1. School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China

    Li-qun Xiao

  2. Department of Statistics and Finance, The School of Management, University of Science and Technology of China, Hefei, 230026, China

    Li-qun Xiao, Zhan-feng Wang & Yao-hua Wu

Authors
  1. Li-qun Xiao
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  2. Zhan-feng Wang
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  3. Yao-hua Wu
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Corresponding author

Correspondence to Zhan-feng Wang.

Additional information

Supported in part by the National Natural Science Foundation of China under (Grant No. 11601097 and 11471302) and the State Key Program of National Natural Science of China (Grant No. 11231010).

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Xiao, Lq., Wang, Zf. & Wu, Yh. Composite Quantile Regression Estimation for Left Censored Response Longitudinal Data. Acta Math. Appl. Sin. Engl. Ser. 34, 730–741 (2018). https://doi.org/10.1007/s10255-018-0782-6

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  • DOI: https://doi.org/10.1007/s10255-018-0782-6

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