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Abstract
Journal of Official Statistics, Vol.19, No.1, 2003. pp. 4557

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Constrained Inverse Adaptive Cluster Sampling

Abstract:
Adaptive cluster sampling can be a useful design for sampling rare and clustered populations. In this article a new adaptive cluster sampling, which is an extension of the classical one, is suggested. It is denominated constrained inverse adaptive cluster sampling and its distinctive characteristic is to make sure that the initial sample contains at least one unit satisfying the condition for extra sampling. This is achieved by means of a sequential selection of the initial sample. This sort of selection of the initial units introduces a bias into the estimators of the mean of the population usually used in the adaptive cluster sampling. To overcome this difficulty two new unbiased estimators of the mean of the population are suggested in the article. The expressions of their variance and of their sample variance estimators are also proposed. To study the properties of the proposed strategies a simulation study is carried out.

Keywords:
Rare and clustered populations; sequential selection.

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