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Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling

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Abstract

This paper considers a competing risks model for right-censored and length-biased survival data from prevalent sampling. We propose a nonparametric quantile inference procedure for cause-specific residual life distribution with competing risks data. We also derive the asymptotic properties of the proposed estimators of this quantile function. Simulation studies and the unemployment data demonstrate the practical utility of the methodology.

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Acknowledgments

The authors appreciate the Editors and two anonymous Reviewers for their constructive comments and insights. We also thank Professor Jacobo De Uña-àlvarez for sharing the Galician unemployment data.

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Authors and Affiliations

  1. School of Economics and Finance, Xi’an Jiaotong University, Xi’an, 710049, China

    Fei-Peng Zhang

  2. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai, 201620, China

    Cai-Yun Fan

  3. Key Laboratory of Advanced Theory and Application in Statistics and Data Science, MOE, & Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, 200062, China

    Yong Zhou

Authors
  1. Fei-Peng Zhang
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  2. Cai-Yun Fan
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  3. Yong Zhou
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Corresponding author

Correspondence to Fei-Peng Zhang.

Additional information

This paper is supported in part by the National Natural Science Foundation of China (Nos. 11771133, 11801360, 91546202, 71931004).

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Zhang, FP., Fan, CY. & Zhou, Y. Nonparametric Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling. Acta Math. Appl. Sin. Engl. Ser. 36, 902–916 (2020). https://doi.org/10.1007/s10255-020-0972-x

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  • DOI: https://doi.org/10.1007/s10255-020-0972-x

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