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Regression estimators in extreme and median ranked set samples

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  • Hassen Muttlak

Abstract

The ranked set sampling (RSS) method as suggested by McIntyre (1952) may be modified to come up with new sampling methods that can be made more efficient than the usual RSS method. Two such modifications, namely extreme and median ranked set sampling methods, are considered in this study. These two methods are generally easier to use in the field and less prone to problems resulting from errors in ranking. Two regression-type estimators based on extreme ranked set sampling (ERSS) and median ranked set sampling (MRSS) for estimating the population mean of the variable of interest are considered in this study and compared with the regression-type estimators based on RSS suggested by Yu & Lam (1997). It turned out that when the variable of interest and the concomitant variable jointly followed a bivariate normal distribution, the regression-type estimator of the population mean based on ERSS dominates all other estimators considered.

Suggested Citation

  • Hassen Muttlak, 2001. "Regression estimators in extreme and median ranked set samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(8), pages 1003-1017.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:1003-1017
    DOI: 10.1080/02664760120076670
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    Cited by:

    1. Wang, You-Gan & Zhu, Min, 2005. "Optimal sign tests for data from ranked set samples," Statistics & Probability Letters, Elsevier, vol. 72(1), pages 13-22, April.
    2. Hakan Savaş Sazak & Melis Zeybek, 2022. "The modified maximum likelihood estimators for the parameters of the regression model under bivariate median ranked set sampling," Computational Statistics, Springer, vol. 37(3), pages 1069-1109, July.

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