Quantile Estimation in Successive Sampling

  • Singh, Housila P. (School of Studies in Statistics Vikram University) ;
  • Tailor, Ritesh (School of Studies in Statistics Vikram University) ;
  • Singh, Sarjinder (Department of Statistics St. Cloud State University) ;
  • Kim, Jong-Min (Statistics, Division of Science and Mathematics University of Minnesota-Morris)
  • Published : 2006.12.01


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.