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Adaptive Sampling DesignsAdaptive Cluster Sampling

Adaptive Sampling Designs: Adaptive Cluster Sampling [One of the main methods of adaptive sampling is adaptive cluster sampling. As it involves unequal probability of sampling, standard Horvitz-Thompson and Hansen-Hurwitz estimators can be modified to provide unbiased estimates of finite population parameters along with unbiased variance estimators. These estimators are compared with each other and with conventional estimators. Confidence intervals are discussed, including bootstrap and empirical likelihood methods, and a biased estimator that we call Hájek’s estimator is described because of its link with this topic. The chapter closes with some theory about selecting networks without replacement.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Sampling DesignsAdaptive Cluster Sampling

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Publisher
Springer Berlin Heidelberg
Copyright
© The Author(s) 2013
ISBN
978-3-642-33656-0
Pages
11 –26
DOI
10.1007/978-3-642-33657-7_2
Publisher site
See Chapter on Publisher Site

Abstract

[One of the main methods of adaptive sampling is adaptive cluster sampling. As it involves unequal probability of sampling, standard Horvitz-Thompson and Hansen-Hurwitz estimators can be modified to provide unbiased estimates of finite population parameters along with unbiased variance estimators. These estimators are compared with each other and with conventional estimators. Confidence intervals are discussed, including bootstrap and empirical likelihood methods, and a biased estimator that we call Hájek’s estimator is described because of its link with this topic. The chapter closes with some theory about selecting networks without replacement.]

Published: Oct 23, 2012

Keywords: Indicator variables; Horvitz-Thompson estimator; Hansen-Hurwitz estimator; Bootstrap; Hájek’s estimator; Empirical likelihood confidence interval; Networks selected without replacement

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