Abstract
In the literature on the quantification of the diversity in a population, one of the indices that has been proven to be useful in dealing with uncensused populations is the Gini-Simpson one. On the other hand, uncensused populations whose diversity with respect to a certain aspect is required to be quantified, often arise naturally stratified and large samples from them are available. Targets of this paper are: to approximate the optimum allocation in estimating the Gini-Simpson diversity in stratified sampling so that the asymptotic precision is maximized; to introduce the relative gain in precision from stratified sampling (with optimum allocation) over random one as a measure of the adequacy of a variate for stratification in approximating diversity; to construct a procedure to test the convenience of a given variate for stratification in estimating diversity from large samples.
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Alonso, M.C., Gil, M.A. (1995). Testing the convenience of a variate for stratification in estimating the Gini-Simpson diversity. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035952
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DOI: https://doi.org/10.1007/BFb0035952
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