Share This Article:

Efficiency of the Adaptive Cluster Sampling Designs in Estimation of Rare Populations

Abstract Full-Text HTML Download Download as PDF (Size:2520KB) PP. 412-418
DOI: 10.4236/ojs.2014.45040    3,032 Downloads   3,755 Views  

ABSTRACT

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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, 4, 412-418. doi: 10.4236/ojs.2014.45040.

References

[1] Dryver, A.L. and Chao, C.T. (2007) Ratio Estimators in Adaptive Cluster Sampling. Environmetrics, 18, 607-620.
http://dx.doi.org/10.1002/env.838
[2] Turki, P. and Barkowski, J.J. (2005) A Review of Adaptive Cluster Sampling: 1990-2003. Environmental and Ecological Statistics, 12, 55-94.
http://dx.doi.org/10.1007/s10651-005-6818-0
[3] Thompson, S.K. (1990) Adaptive Cluster Sampling. Journal of American Statistical Association, 85, 1054-1059.
http://dx.doi.org/10.1080/01621459.1990.10474975
[4] Noon, B.R., Ishwar, N.M. and Vasudevan, K. (2006) Efficiency of Adaptive Cluster and Random Sampling in Detecting Terrestrial Herpetofauna in a Tropical Rainforest. Wildlife Society Bulletin, 34, 59-68.
http://dx.doi.org/10.2193/0091-7648(2006)34[59:EOACAR]2.0.CO;2
[5] Philippi, T. (2005) Adaptive Cluster Sampling for Estimation of Abundances within Local Populations of Low Abundance Plants. Ecology, 86, 1091-1100.
http://dx.doi.org/10.1890/04-0621
[6] Kenya Wildlife Service (2010) Aerial Total Count: Amboseli-West Kilimanjaro/Magadi-Natron Cross Border Land Scape.
http://www.kws.org/export/sites/kws/info/publications/census_reports/Amboseli_West_kili_Magadi_
Natron_2010_cesus_report.pdf
[7] Smith, D.R., Brown, J.A. and Lo, N.C.H. (2004) Application of Adaptive Cluster Sampling to Biological Populations. In: Thompson, W.L., Ed., Sampling Rare or Elusive Species, Island Press, Covelo, 93-152.
[8] Diggle, D.J. (1983) Statistical Analysis of Spatial Point Patterns. Academic Press, London.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.