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Jointly Type-II Censored Length-Biased Exponential Distributions

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Mathematical Methods for Engineering Applications (ICMASE 2021)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 384))

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Abstract

This paper deals with the jointly Type-II censored length-biased exponential populations. In this study, after introducing the jointly Type-II censoring scheme, we first obtained the maximum likelihood estimations of the unknown scale parameters with their asymptotic confidence intervals. Then, the Bayesian estimations of the parameters are obtained by using the importance sampling method. Further, the highest posterior density credible intervals of the Bayesian estimations are provided. The simulation studies are performed to evaluate the performances of the estimation methods. Finally, a numerical example is used to illustrate the theoretical outcomes.

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Authors and Affiliations

  1. Department of Accounting and Taxation, Bingöl University, Bingöl, Turkey

    Çağatay Çetinkaya

Authors
  1. Çağatay Çetinkaya
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Correspondence to Çağatay Çetinkaya .

Editor information

Editors and Affiliations

  1. Faculty of Arts and Sciences, Ankara Hacı Bayram Veli University, Ankara, Turkey

    Fatih Yilmaz

  2. Institute of Fundamental Physics and Mathematics, University of Salamanca, Salamanca, Spain

    Araceli Queiruga-Dios

  3. Faculty of Sciences, University of Salamanca, Salamanca, Spain

    María Jesús Santos Sánchez

  4. Polytechnic Institute of Coimbra (ISEC), Coimbra, Portugal

    Deolinda Rasteiro

  5. Institute of Physical and Information Technologies (ITEFI), Spanish National Research Council (CSIC), Madrid, Spain

    Víctor Gayoso Martínez

  6. Institute of Fundamental Physics and Mathematics, University of Salamanca, Salamanca, Spain

    Jesús Martín Vaquero

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Çetinkaya, Ç. (2022). Jointly Type-II Censored Length-Biased Exponential Distributions. In: Yilmaz, F., Queiruga-Dios, A., Santos Sánchez, M.J., Rasteiro, D., Gayoso Martínez, V., Martín Vaquero, J. (eds) Mathematical Methods for Engineering Applications. ICMASE 2021. Springer Proceedings in Mathematics & Statistics, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-030-96401-6_5

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