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Variance estimation in the presence of imputed data for high entropy sampling designs

Auteur(s)
Haziza, David
Vallée, Audrey-Anne 
Institut de statistique 
Maison d'édition
Neuchâtel Université de Neuchâtel Institut de Statistique
Date de parution
2014
Nombre de page
22
Mots-clés
  • High entropy sampling design
  • imputation
  • nonresponse
  • second-order inclusion probabilities
  • variance estimation.
  • High entropy sampling...

  • imputation

  • nonresponse

  • second-order inclusio...

  • variance estimation.

Résumé
Variance estimation is an important aspect of the estimation process in statistical agencies as variance estimates provide a measure of the quality of the survey’s estimates. In the presence of imputed data, usual variance estimators rely on the availability of the second-order inclusion
probabilities, which may be difficult (even impossible) to compute for ome sampling designs. In this paper, we derive simplified variance estimators that result from approximating the second-order inclusion probabilities in terms of the first-order inclusion probabilities. Results
of a simulation study, evaluating the properties of the proposed variance estimators in terms of bias and mean squared error, will be presented.
Identifiants
https://libra.unine.ch/handle/123456789/22523
Type de publication
working paper
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