• Title/Summary/Keyword: finite population quantile

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Quantile Estimation in Successive Sampling

  • Singh, Housila P.;Tailor, Ritesh;Singh, Sarjinder;Kim, Jong-Min
    • Proceedings of the Korean Association for Survey Research Conference
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    • 2006.12a
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    • pp.67-83
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    • 2006
  • In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

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QUANTILE ESTIMATION IN SUCCESSIVE SAMPLING

  • Singh, Housila P.;Tailor, Ritesh;Singh, Sarjinder;Kim, Jong-Min
    • Journal of the Korean Statistical Society
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    • v.36 no.4
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    • pp.543-556
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    • 2007
  • In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.