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Calibrated random imputation for qualitative data

Anne-Catherine Favre, Alina Matei & Yves Tillé

Abstract In official statistics, when a file of microdata must be delivered to external users, it is very difficult to propose them a file where missing values has been treated by multiple imputations. In order to overcome this difficulty, we propose a method of single imputation for qualitative data that respect numerous constraints. The imputation is balanced on totals previously estimated; editing rules can be respected; the imputation is random, but the totals are not affected by an imputation variance.
   
Keywords
   
Citation Favre, A. C., Matei, A., & Tillé, Y. (2005). Calibrated random imputation for qualitative data. Journal of Statistical Planning and Inference, 128(2), 411-425.
   
Type Journal article (English)
Date of appearance 23-3-2005
Journal Journal of Statistical Planning and Inference
Volume 128
Issue 2
Pages 411-425
URL http://www.sciencedirect.com/science/article/pii/S0378375...