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Semiparametric quantile-difference estimation for length-biased and right-censored data

  • Yutao Liu
  • Shucong Zhang
  • Yong Zhou
Articles
  • 5 Downloads

Abstract

Prevalent cohort studies frequently involve length-biased and right-censored data, a fact that has drawn considerable attention in survival analysis. In this article, we consider survival data arising from lengthbiased sampling, and propose a new semiparametric-model-based approach to estimate quantile differences of failure time. We establish the asymptotic properties of our new estimators theoretically under mild technical conditions, and propose a resampling method for estimating their asymptotic variance. We then conduct simulations to evaluate the empirical performance and efficiency of the proposed estimators, and demonstrate their application by a real data analysis.

Keywords

quantile differences length-biased sampling right-censored proportional hazards model 

MSC(2010)

62N01 62N02 

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Notes

Acknowledgements

Liu's work was supported by National Natural Science Foundation of China (Grant No. 11401603), the Fundamental Research Funds for the Central Universities (Grant No. QL 18009) and Discipline Foundation of Central University of Finance and Economics (Grant No. CUFESAM201811). Zhou's work was supported by the State Key Program of National Natural Science Foundation of China (Grant No. 71331006) and the State Key Program in the Major Research Plan of National Natural Science Foundation of China (Grant No. 91546202). The authors thank two anonymous referees for many helpful comments and suggestions, which have greatly improved the paper.

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Copyright information

© Science in China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Statistics and MathematicsCentral University of Finance and EconomicsBeijingChina
  2. 2.School of Statistics and ManagementShanghai University of Finance and EconomicsShanghaiChina
  3. 3.Key Laboratory of Advanced Theory and Application in Statistics and Data Science (MOE), Institute of Statistics and Interdisciplinary Sciences and School of StatisticsEast China Normal UniversityShanghaiChina

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