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Fast Balanced Sampling for Highly Stratified Population

Auteur(s)
Hasler, Caren 
Institut de statistique 
TillĂ©, Yves 
Institut de statistique 
Date de parution
2014-6
In
Computational Statistics and Data Analysis
No
74
De la page
81
A la page
94
Mots-clés
  • Balanced sampling
  • Stratified sampling
  • Cube method
  • Unequal probability sampling
  • Auxiliary information
  • Balanced sampling

  • Stratified sampling

  • Cube method

  • Unequal probability s...

  • Auxiliary information...

Résumé
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.
Lié au projet
Convention UniversitĂ© de Neuchâtel/Office fĂ©dĂ©ral de la statistique 
Identifiants
https://libra.unine.ch/handle/123456789/11133
Type de publication
journal article
Dossier(s) à télécharger
 main article: 2019-09-11_951_4779.pdf (441.74 KB)
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