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Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets

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  • Syed Mohsin Ali Kazmi
  • Muhammad Aslam
  • Sajid Ali
  • Nasir Abbas

Abstract

This paper explores the study on mixture of a class of probability density functions under type-I censoring scheme. In this paper, we mold a heterogeneous population by means of a two-component mixture of the class of probability density functions. The parameters of the class of mixture density functions are estimated and compared using the Bayes estimates under the squared-error and precautionary loss functions. A censored mixture dataset is simulated by probabilistic mixing for the computational purpose considering particular case of the Maxwell distribution. Closed-form expressions for the Bayes estimators along with their posterior risks are derived for censored as well as complete samples. Some stimulating comparisons and properties of the estimates are presented here. A factual dataset has also been for illustration.

Suggested Citation

  • Syed Mohsin Ali Kazmi & Muhammad Aslam & Sajid Ali & Nasir Abbas, 2013. "Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1639-1658, August.
  • Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1639-1658
    DOI: 10.1080/02664763.2013.789831
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    Cited by:

    1. Sajid Ali & Muhammad Riaz, 2014. "On the generalized process capability under simple and mixture models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 832-852, April.
    2. Jafer Rahman & Muhammad Aslam, 2017. "On estimation of two-component mixture inverse Lomax model via Bayesian approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 99-109, January.

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