Abstract
While conducting a sample survey, a number of difficult sampling problems are encountered. One of them is the problem in estimating the population mean/total when it is rare or geographically uneven. If the population of interest is hidden or elusive, then it becomes difficult to identify it for sampling.
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References
Cochran, W.G.: Sampling Techniques, 3rd edn. Wiley, New York (1977)
Dryver, A.L., Thompson, S.K.: Improving Unbiased Estimators in Adaptive Cluster Sampling. Technical Report, Department of Statistics, Pennsylvania State University (1998a)
Dryver, A.L., Thompson, S.K.: Adaptive Cluster Sampling Without Replacement of Clusters. Technical Report 9, Department of Statistics, Pennsylvania State University (1998b)
Elliott, M.R., Little, R.J.A.: Model-based alternatives to trimming survey weights. J. Off. Stat. 16(3), 191–209 (2000)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley Series in Probability and Statistics, 2nd edn. Wiley, New York (2002)
Thompson, S.K.: Adaptive cluster sampling. J. Am. Stat. Assoc. 85(412), 1050–1058 (1990)
Thompson, S.K., Seber, G.A.F.: Adaptive Sampling. Wiley, New York (1996)
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Latpate, R., Kshirsagar, J., Kumar Gupta, V., Chandra, G. (2021). Adaptive Cluster Sampling. In: Advanced Sampling Methods. Springer, Singapore. https://doi.org/10.1007/978-981-16-0622-9_10
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DOI: https://doi.org/10.1007/978-981-16-0622-9_10
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