Adaptive cluster sampling (ACS) has been a
very important tool in estimation of population parameters of rare and
clustered population. The fundamental idea behind this sampling plan is to decide
on an initial sample from a defined population and to keep on sampling within
the vicinity of the units that satisfy the condition that at least one
characteristic of interest exists in a unit selected in the initial sample.
Despite being an important tool for sampling rare and clustered population,
adaptive cluster sampling design is unable to control the final sample size
when no prior knowledge of the population is available. Thus adaptive cluster
sampling with data-driven stopping rule (ACS’) was proposed to control the
final sample size when prior knowledge of population structure is not
available. This study examined the behavior of the HT, and HH estimator under
the ACS design and ACS’ design using artificial population that is designed to
have all the characteristics of a rare and clustered population. The efficiencies
of the HT and HH estimator were used to determine the most efficient design in
estimation of population mean in rare and clustered population. Results of both
the simulated data and the real data show that the adaptive cluster sampling
with stopping rule is more efficient for estimation of rare and clustered
population than ordinary adaptive cluster sampling.
Cite this paper
Mwangi, C. , Islam, A. and Orawo, L. (2014) Efficiency of the Adaptive Cluster Sampling Designs in Estimation of Rare Populations. Open Journal of Statistics
, 412-418. doi: 10.4236/ojs.2014.45040
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