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Two-Stage Adaptive Cluster Sampling
Biometrics, 1997Summary: Adaptive cluster sampling is a powerful method for parameter estimation when a population is highly clumped with clumps widely separated. Unfortunately, its use has been somewhat limited until now because of the lack of a suitable theory for using a pilot survey to design an experiment with a given efficiency or expected cost.
Salehi M., Mohammad, Seber, George A. F.
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Bootstrap Confidence Intervals for Adaptive Cluster Sampling
Biometrics, 2000Summary.Consider a collection of spatially clustered objects where the clusters are geographically rare. Of interest is estimation of the total number of objects on the site from a sample of plots of equal size. Under these spatial conditions, adaptive cluster sampling of plots is generally useful in improving efficiency in estimation over simple ...
Christman, Mary C., Pontius, Jeffrey S.
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Restricted adaptive cluster sampling
Environmental and Ecological Statistics, 1998Adaptive cluster sampling can be a useful design for sampling rare and patchy populations. With this design the initial sample size is fixed but the size of the final sample (and total sampling effort) cannot be predicted prior to sampling. For some populations the final sample size can be quite variable depending on the level of patchiness. Restricted
J. A. Brown, B. J. F. Manly
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2012
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.
George A. F. Seber, Mohammad M. Salehi
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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.
George A. F. Seber, Mohammad M. Salehi
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Adaptive Sampling for k-Means Clustering
2009We show that adaptively sampled O (k ) centers give a constant factor bi-criteria approximation for the k -means problem, with a constant probability. Moreover, these O (k ) centers contain a subset of k centers which give a constant factor approximation, and can be found using LP-based techniques of Jain and Vazirani [JV01] and Charikar et al. [CGTS02]
Ankit Aggarwal +2 more
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Two-Stage Adaptive Cluster Sampling
2021Adaptive sampling is a method in which selection of units at any stage of sampling depends upon the information collected from the already selected units in the initial sample. It means, if one finds what he/she is looking for at a particular location then he/she would sample in the vicinity of that location with the hope of obtaining more information.
Raosaheb Latpate +3 more
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Adaptive cluster sampling with a data driven stopping rule
Statistical Methods & Applications, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
GATTONE, Stefano Antonio +1 more
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Negative Adaptive Cluster Double Sampling
2021We have discussed the methods of ACDS and NACS in detail in the previous chapter. In this chapter, we have proposed another new method for estimating the mean/total of the variable of interest. This method is a two-phase variant of the NACS obtained by combining the idea of double sampling and NACS.
Raosaheb Latpate +3 more
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Stratified Inverse Adaptive Cluster Sampling
2021In forestry and environmental sciences, some species of plants and animals are rare and clustered, i.e., abundance of zeros. The traditional sampling methods provide poor estimates of the population mean/total. In such situations, adaptive sampling is useful.
Raosaheb Latpate +3 more
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Asymptotics in adaptive cluster sampling
Environmental and Ecological Statistics, 2003In this article we consider asymptotic properties of the Horvitz-Thompson and Hansen-Hurwitz types of estimators under the adaptive cluster sampling variants obtained by selecting the initial sample by simple random sampling without replacement and by unequal probability sampling with replacement.
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