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Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring

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  • Julian Górny

    (RWTH Aachen University)

  • Erhard Cramer

    (RWTH Aachen University)

Abstract

In this paper, a structural analysis of hybrid censoring models is presented. This new modularization approach to hybrid censoring models enables a convenient derivation of distributional results. For instance, it allows to derive the exact distribution of the MLEs under an exponential assumption for very complex hybrid scenarios. In order to illustrate the benefit of this idea, we apply it to four new unified progressive hybrid censoring schemes. They are extensions of already proposed unified Type-I/II/III/IV hybrid censoring schemes to progressively Type-II censored data. The resulting analysis shows that the modularization approach provides a powerful, efficient, and elegant tool to study even more complex hybrid censoring models.

Suggested Citation

  • Julian Górny & Erhard Cramer, 2018. "Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(2), pages 173-210, February.
    • Handle: RePEc:spr:metrik:v:81:y:2018:i:2:d:10.1007_s00184-017-0639-7
    DOI: 10.1007/s00184-017-0639-7
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    References listed on IDEAS

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    1. Balakrishnan, N. & Kundu, Debasis, 2013. "Hybrid censoring: Models, inferential results and applications," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 166-209.
    2. A. Childs & B. Chandrasekar & N. Balakrishnan & D. Kundu, 2003. "Exact likelihood inference based on Type-I and Type-II hybrid censored samples from the exponential distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 319-330, June.
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    Cited by:

    1. van Bentum, Thomas & Cramer, Erhard, 2019. "Stochastic monotonicity of MLEs of the mean for exponentially distributed lifetimes under hybrid censoring," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 1-8.
    2. Wang, Liang & Tripathi, Yogesh Mani & Lodhi, Chandrakant & Zuo, Xuanjia, 2022. "Inference for constant-stress Weibull competing risks model under generalized progressive hybrid censoring," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 70-83.

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