Adaptive cluster sampling based on balanced sampling plan excluding contiguous units

Abstract

The method of adaptive cluster sampling (ACS) is useful for the surveys when the characteristic of interest is sparsely distributed but highly aggregated. In ACS, the initial sample is selected by simple random sampling (SRS). The final sample is obtained by adapting the nearby units which satisfy the pre-specified condition. This sampling can further be improved when the appropriate control is imposed to select the initial sample. In this article, Balanced Sampling plan Excluding Contiguous units (BSEC plan) has been used to select the initial sample for estimating population mean in ACS. The purpose is to ignore non preferred or contiguous units and include more preferred units in the final sample. The unbiased estimators of population mean based on the initial sample and modified Hansen-Hurwitz (HH) type estimators are discussed. It has been illustrated theoretically and empirically that the modified HH type estimator based on BSEC plan is more precise than that based on SRS. The proposed method provides the opportunity to select the non contiguous units in initial sample, which makes the design more informative and efficient in comparison to ACS. The proposed design may be helpful in improving the ACS estimates in many real-life situations.

Keywords:

• Contiguous units
• controlled sampling
• BSEC plan
• ranked set sampling
• adaptive cluster sampling
• Hansen-Hurwitz estimator

Mathematics Subject Classification:

• 62D05
• 62G09
• 94A20

Acknowledgment

The authors are grateful to both the anonymous referees for their innovative and constructive comments and suggestions, which led to the considerable improvement in presentation of this manuscript.

Disclosure statement

All authors declare that they have no conflicts of interest.