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Jackknife empirical likelihood inference for the accelerated failure time model

Author

Listed:
  • Xue Yu

    (Georgia State University)

  • Yichuan Zhao

    (Georgia State University)

Abstract

Accelerated failure time (AFT) model is a useful semi-parametric model under right censoring, which is an alternative to the commonly used proportional hazards model. Making statistical inference for the AFT model has attracted considerable attention. However, it is difficult to compute the estimators of regression parameters due to the lack of smoothness for rank-based estimating equations. Brown and Wang (Stat Med 26(4):828–836, 2007) used an induced smoothing approach, which smooths the estimating functions to obtain point and variance estimators. In this paper, a more computationally efficient method called jackknife empirical likelihood (JEL) is proposed to make inference for the accelerated failure time model without computing the limiting variance. Results from extensive simulation suggest that the JEL method outperforms the traditional normal approximation method in most cases. Subsequently, two real data sets are analyzed for illustration of the proposed method.

Suggested Citation

  • Xue Yu & Yichuan Zhao, 2019. "Jackknife empirical likelihood inference for the accelerated failure time model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 269-288, March.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:1:d:10.1007_s11749-018-0601-7
    DOI: 10.1007/s11749-018-0601-7
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    References listed on IDEAS

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    9. Zhouping Li & Jinfeng Xu & Wang Zhou, 2016. "On Nonsmooth Estimating Functions via Jackknife Empirical Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 49-69, March.
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    Cited by:

    1. Faysal Satter & Yichuan Zhao & Ni Li, 2024. "Empirical likelihood inference for the panel count data with informative observation process," Statistical Papers, Springer, vol. 65(5), pages 3039-3061, July.

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