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Adaptive Sampling Designs pp 37–47Cite as

Primary and Secondary Units

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Abstract

In adaptive sampling, the sampling units can sometimes be divided into primary and secondary units. After a sample of primary units is taken, adaptive cluster sampling can be carried out within each primary unit selected using either a sample or all of its secondary units. Primary units can be a variety of shapes such as strip transects or Latin squares. Two procedures are possible depending on whether adaptive clusters are allowed to cross primary unit boundaries or not. Stratified sampling is a special case in which all the primary units or strata are sampled. Two stage-sampling can be used for carrying out a pilot survey to determine how to design a full-scale survey.

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References

  • Acharya, B., A. Bhattarai, A. De Gier, and A. Stein. 2000. “Systematic Adaptive Cluster Sampling for the Assessment of Rare Tree Species in Nepal.” Forest Ecology and Management 137:65–73.

    Article  Google Scholar 

  • Borkowski, J.J. 1999. “Network Inclusion Probabilities and Horvitz-Thompson Estimation for Adaptive Simple Latin Square Sampling.” Environmental and Ecological Statistics 6:291–311.

    Article  Google Scholar 

  • Borkowski, J.J. 2003. “Simple Latin Square Sampling \(\pm k\) Designs.” Communications in Statistics—Theory and Methods 32:215–237.

    Article  MathSciNet  MATH  Google Scholar 

  • Christman, M.C. 2000. “A Review of Quadrat-Based Sampling of Rare, Geographically Clustered Populations.” Journal of Agricultural, Biological, and Environmental Statistics 5:168–201.

    Article  MathSciNet  Google Scholar 

  • Félix-Medina, M.H., and S.K. Thompson. 2004. “Adaptive Cluster Double Sampling.” Biometrika 91:877–891.

    Google Scholar 

  • Hung, Y. 2011. “Adaptive Probability-Based Latin Hypercube Designs.” Journal of the American Statistical Association, Theory and Methods 106(493):213–219. DOI: 10.1198/jasa.2011.tm10337.

    Article  Google Scholar 

  • Munholland, P.L. and J.J. Borkowski. 1993. Adaptive Latin square sampling \(+\)1 designs. Technical Report No. 3-23-93, Department of Mathematical Sciences, Montana State University, Bozeman.

    Google Scholar 

  • Munholland, P.L., and J.J. Borkowski. 1996. “Latin Square Sampling \(+\)1 Designs.” Biometrics 52:125–136.

    Article  MATH  Google Scholar 

  • Pollard, J.H., D. Palka, and S.T. Buckland. 2002. “Adaptive Line Transect Sampling.” Biometrics 58(4): 862–870.

    Article  MathSciNet  MATH  Google Scholar 

  • Rocco, E. 2008. “Two-Stage Restricted Adaptive Cluster Sampling.” METRON-International Journal of Statistics LXVI (3):313–327.

    Google Scholar 

  • Salehi, M.M. 2002. “Systematic Simple Latin Square Sampling (\(+\)1) Design and Its Optimality.” Journal of Propagations on Probability and Statistics 2:191–200.

    Google Scholar 

  • Salehi, M.M. 2004. “Optimal Sampling Design under a Spatial Correlation Model.” Journal of Statistical Planning and Inference 118:8–19.

    Google Scholar 

  • Salehi, M.M. 2006. “Row and Column Elimination Sampling \(+\)1 Design.” Communications in Statistics, Theory and Methods 35(2): 349–362.

    Article  MathSciNet  MATH  Google Scholar 

  • Salehi, M.M., and G.A.F. Seber. 1997. “Two-Stage Adaptive Cluster Sampling.” Biometrics 53:959–970.

    Article  MATH  Google Scholar 

  • Salehi, M.M., and G.A.F. Seber. 2002. “Unbiased Estimators for Restricted Adaptive Cluster Sampling.” Australian and New Zealand Journal of Statistics 44:63–74.

    Article  MathSciNet  MATH  Google Scholar 

  • Särndal, C.E., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag.

    Book  MATH  Google Scholar 

  • Thompson, S.K. 1991a. “Adaptive Cluster Sampling: Designs with Primary and Secondary Units.” Biometrics 47:1103–1115.

    Article  Google Scholar 

  • Thompson, S.K. 1991b. “Stratified Adaptive Cluster Sampling.” Biometrika 78:389–397.

    Article  MathSciNet  Google Scholar 

  • Thompson, S.K., and G.A.F. Seber. 1996. Adaptive Sampling. New York: Wiley.

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1.  , 7 Milford Park Place, 0620, North Shore, New Zealand

    George A. F. Seber

  2. Department of Statistics, University of Auckland, Auckland, New Zealand

    George A. F. Seber

  3. Department of Mathematics, Statistics and Physics, Qatar University, Doha, 2713, Qatar

    Mohammad M. Salehi

Authors
  1. George A. F. Seber
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  2. Mohammad M. Salehi
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Correspondence to George A. F. Seber .

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Seber, G.A.F., Salehi, M.M. (2012). Primary and Secondary Units. In: Adaptive Sampling Designs. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33657-7_4

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