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Tailor, Rajesh,Parmar, Rajesh,Kim, Jong-Min,Tailor, Ritesh The Korean Statistical Society 2011 Communications for statistical applications and me Vol.18 No.2
This paper suggests two ratio-cum-product estimators of finite population mean using known coefficient of variation and co-efficient of kurtosis of auxiliary characters. The bias and mean squared error of the proposed estimators with large sample approximation are derived. It has been shown that the estimators suggested by Upadhyaya and Singh (1999) are particular case of the suggested estimators. Almost ratio-cum product estimators of suggested estimators have also been obtained using Jackknife technique given by Quenouille (1956). An empirical study is also carried out to demonstrate the performance of the suggested estimators.
Quantile Estimation in Successive Sampling
Housila P. Singh,Ritesh Tailor,Sarjinder Singh,김종민 한국통계학회 2007 Journal of the Korean Statistical Society Vol.36 No.4
In successive sampling on two ccasions the problem of estimating a -nite population quantile has been considered. The theory developed aimsat providing the optimum estimates by combining (i) three double sam-pling estimatorsviz.ratio-type, product-type and regression-type, from thematched portion of the sample and (ii) a simple quantile based on a randomsample from the unmatched portion of the sample on the second occasion.The approximate variance formulae of the suggested estimators have beenobtained. Optimal matching fraction is discussed. A simulation study iscarried out in order to compare the three estimators and direct estimator.It is found that the performance of the regression-type estimator is the bestamong all the estimators discussed here.
QUANTILE ESTIMATION IN SUCCESSIVE SAMPLING
Singh, Housila P.,Tailor, Ritesh,Singh, Sarjinder,Kim, Jong-Min The Korean Statistical Society 2007 Journal of the Korean Statistical Society Vol.36 No.4
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.