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Estimating moments of a selected Pareto population under asymmetric scale invariant loss function

Author

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  • Riyadh Rustam Al-Mosawi

    (Thiqar University)

  • Shahjahan Khan

    (University of Southern Queensland)

Abstract

Consider independent random samples from $$(k\ge 2)$$ ( k ≥ 2 ) Pareto populations with the same known shape parameter but different scale parameters. Let $$X_i$$ X i be the smallest observation of the ith sample. The natural selection rule which selects the population associated with the largest $$X_i$$ X i is considered. In this paper, we estimate the moments of the selected population under asymmetric scale invariant loss function. We investigate risk-unbiased, consistency and admissibility of the natural estimators for the moments of the selected population. Finally, the risk-bias’s and risks of the natural estimators are numerically computed and compared for $$k=2,3.$$ k = 2 , 3 .

Suggested Citation

  • Riyadh Rustam Al-Mosawi & Shahjahan Khan, 2018. "Estimating moments of a selected Pareto population under asymmetric scale invariant loss function," Statistical Papers, Springer, vol. 59(1), pages 183-198, March.
    • Handle: RePEc:spr:stpapr:v:59:y:2018:i:1:d:10.1007_s00362-016-0758-7
    DOI: 10.1007/s00362-016-0758-7
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    References listed on IDEAS

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    5. Kumar, Somesh & Mahapatra, Ajaya Kumar & Vellaisamy, P., 2009. "Reliability estimation of the selected exponential populations," Statistics & Probability Letters, Elsevier, vol. 79(11), pages 1372-1377, June.
    6. Neeraj Misra & Edward Meulen, 2003. "On estimating the mean of the selected normal population under the LINEX loss function," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 58(2), pages 173-183, September.
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    Cited by:

    1. Lakshmi Kanta Patra & Suchandan Kayal & Somesh Kumar, 2020. "Estimating a function of scale parameter of an exponential population with unknown location under general loss function," Statistical Papers, Springer, vol. 61(6), pages 2511-2527, December.

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