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Statistical Analysis of Location Parameter of Inverse Gaussian Distribution Under Noninformative Priors

Author

Listed:
  • Nida Khan

    (Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan)

  • Muhammad Aslam

    (Department of Mathematics and Statistics, Riphah International University, Islamabad, Pakistan.)

Abstract

Bayesian estimation for location parameter of the inverse Gaussian distribution is presented in this paper. Noninformative priors (Uniform and Jeffreys) are assumed to be the prior distributions for the location parameter as the shape parameter of the distribution is considered to be known. Four loss functions: Squared error, Trigonometric, Squared logarithmic and Linex are used for estimation. Bayes risks are obtained to find the best Bayes estimator through simulation study and real life data.

Suggested Citation

  • Nida Khan & Muhammad Aslam, 2019. "Statistical Analysis of Location Parameter of Inverse Gaussian Distribution Under Noninformative Priors," Journal of Quantitative Methods, University of Management and Technology, Lahore, Pakistan, vol. 3(2), pages 62-76.
    • Handle: RePEc:ris:jqmumt:0028
    as

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    References listed on IDEAS

    as
    1. Sanaa Ismail & Hesham Auda, 2006. "Bayesian and fiducial inference for the inverse gaussian distribution via Gibbs sampler," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(8), pages 787-805.
    2. Sinha, S. K., 1986. "Bayesian estimation of the reliability function of the inverse gaussian distribution," Statistics & Probability Letters, Elsevier, vol. 4(6), pages 319-323, October.
    3. Tiefeng Ma & Shuangzhe Liu & S. Ahmed, 2014. "Shrinkage estimation for the mean of the inverse Gaussian population," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 733-752, August.
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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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