Abstract
In this paper, we study the estimators of the population mean in stratified adaptive cluster sampling by using the information of the auxiliary variable. Simulations showed that if the variable of interest (y) and the auxiliary variables (x,z) have high positive correlation then the estimate of the mean square error of the ratio estimators is less than the estimate of the mean square error of the product estimator. The estimators which use only one auxiliary variable were better than the estimators which use two auxiliary variables.
Highlights
Adaptive cluster sampling, proposed by Thompson [1], is an efficient method for sampling rare and hidden clustered populations
Have high positive correlation the estimate of the mean square error of the ratio estimators is less than the estimate of the mean square error of the product estimator
The estimators which use only one auxiliary variable were better than the estimators which use two auxiliary variables
Summary
Adaptive cluster sampling, proposed by Thompson [1], is an efficient method for sampling rare and hidden clustered populations. If any other units that are “adaptively” added satisfy the condition C , their neighborhoods are added to the sample. 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. An initial stratified sample is selected from a population, and whenever the variable of interest for any unit is observed to satisfy the condition, the neighborhood of that unit is added in the sample. We will study the estimator of population mean in stratified adaptive cluster sampling using an auxiliary.
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