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Fast Balanced Sampling for Highly Stratified Population
Abstract Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. Chauvet (2009) proposed a procedure to select balanced samples in a stratified population. Unfortunately, Chauvet's procedure can be slow when the number of strata is very large. In this paper, we propose a new algorithm to select balanced samples in a stratified population. This new procedure is at the same time faster and more accurate than Chauvet's. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. This algorithm turns out to be valuable for many applications. For instance, it can improve the quality of the estimates produced by multistage surveys for which only one or two primary sampling units are selected in each stratum. Moreover, this algorithm may be used to treat nonresponse.
   
Keywords Balanced sampling, Stratified sampling, Cube method, Unequal probability sampling, Auxiliary information
   
Citation Hasler, C., & Tillé, Y. (2014). Fast Balanced Sampling for Highly Stratified Population. Computational Statistics and Data Analysis, 74, 81-94.
   
Type Journal article (English)
Date of appearance 6-2014
Journal Computational Statistics and Data Analysis
Volume 74
Pages 81-94
URL http://www.sciencedirect.com/science/article/pii/S0167947...
Related project Convention Université de Neuchâtel/Office fédéral de la s...