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Gini index estimation for lifetime data

Author

Listed:
  • Xiaofeng Lv

    (Southwestern University of Finance and Economics)

  • Gupeng Zhang

    (University of Chinese Academy of Science
    Chinese Academy of Science)

  • Guangyu Ren

    (Capital University of Economics and Business)

Abstract

Lifetime data is often right-censored. Recent literature on the Gini index estimation with censored data focuses on independent censoring. However, the censoring mechanism is likely to be dependent censoring in practice. This paper proposes two estimators of the Gini index under independent censoring and covariate-dependent censoring, respectively. The proposed estimators are consistent and asymptotically normal. We also evaluate the performance of our estimators in finite samples through Monte Carlo simulations. Finally, the proposed methods are applied to real data.

Suggested Citation

  • Xiaofeng Lv & Gupeng Zhang & Guangyu Ren, 2017. "Gini index estimation for lifetime data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 275-304, April.
    • Handle: RePEc:spr:lifeda:v:23:y:2017:i:2:d:10.1007_s10985-016-9357-0
    DOI: 10.1007/s10985-016-9357-0
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    Cited by:

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    2. Kattumannil, Sudheesh K. & Dewan, Isha & N., Sreelaksmi, 2021. "Non-parametric estimation of Gini index with right censored observations," Statistics & Probability Letters, Elsevier, vol. 175(C).

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