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Investigating the Efficiency of Stratified Ranked Set Sampling Using Nonparametric Bootstrap Estimation

Year: 2011       Vol.: 60       No.: 1      

Authors: Kevin Carl P. Santos; Jenniebie Salagubang

Abstract:

This paper aims to compare stratified random sampling and stratified ranked set sampling. A simulation study is conducted to evaluate the performance of the parameter estimates on both sampling techniques. Population sizes, sampling rates, stratum sizes, and correlation of the target variable and concomitant variable were varied, nonparametric bootstrap was then used in estimating the mean and its standard error. The coefficient of variation (CV) and the bias of the bootstrap estimates were compared. Stratified ranked set sampling generally outperforms stratified random sampling in terms of bias most especially for small populations. The two sampling designs were used in estimating the average mango production per barangay in the country.

Keywords: ranked set sampling; nonparametric bootstrap estimation; stratification; simple random sampling

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