Abstract
In this study we study the estimators of the population mean in adaptive cluster sampling by using the information of the auxiliary variable. The estimators in this study are the classical ratio estimator, the ratio estimator using the population coefficient of variation and the coefficient of kurtosis of the auxiliary variable, the regression estimator and the difference estimator. Simulations showed that the difference estimator had the smallest estimated mean square error when compared to the ratio estimators and the regression estimator.
Highlights
Adaptive cluster sampling, proposed by Thompson (1990), is an efficient method for sampling rare and hidden clustered populations
In this study we study the estimators of the population mean in adaptive cluster sampling by using the information of the auxiliary variable
Simulations showed that the difference estimator had the smallest estimated mean square error when compared to the ratio estimators and the regression estimator
Summary
Adaptive cluster sampling, proposed by Thompson (1990), is an efficient method for sampling rare and hidden clustered populations. If the value of the variable of interest from a sampled unit satisfies a pre-specified condition C, that is {i, yi ≥ c}, the unit’s neighborhood will be added to the sample. If any other units that are “adaptively” added satisfy the condition C, their neighborhoods are added to the sample. The set of all units selected and all neighboring units that satisfy the condition is called a network. The adaptive sample units, which do not satisfy the condition are called edge units. If a unit is selected in the initial sample and does not satisfy the condition C, there is only one unit in the network. We will study the estimator of population mean in adaptive cluster sampling using an auxiliary variable.
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